Author: Ramkumar Sundarakalatharan

How To Measure Real Success In Software Engineering

How To Measure Real Success In Software Engineering

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Recently, while attending The Business Show in London, I engaged in a conversation with a CXO of an upcoming Fintech company. The discussion began with cybersecurity implementation—a topic close to my heart—but quickly veered into the realm of engineering throughput. What followed was an incoherent rant by the CXO, a frustrating narrative about firing their Delivery Director for refusing to scale the engineering team to meet deadlines for the company’s next shiny event. Despite my best efforts to pull this gentleman out of his rabbit hole, my time and reasoning seemed to fall on deaf ears.

Reflecting on this interaction over the past month, I’ve realized this episode was emblematic of a larger issue: the prevalent fallacy among CXOs that more engineers equals faster and better output. Surprisingly, this misconception thrives in part because of the silence of engineering leaders—CTOs, VPs, and Directors of Engineering—who often fail to push back against flawed assumptions at the executive level.

Inspired by my recent association with the Information Security Group (ISG) at the Royal Holloway University of London, I decided to don my “academic specs” and examine this fallacy more critically. The result is a deeper dive into the myths of scaling engineering teams, the science behind team efficiency, and a call for a cultural shift in how organizations measure productivity.

The Scaling Myth: Why More Isn’t Always Better

At the heart of this fallacy is a simplistic assumption: more engineers means more features, delivered faster. While this notion seems logical, it is disproven by Price’s Law, a principle that exposes the diminishing returns of team scaling.

Rediscovering Derek J. de Solla Price: From Antikythera to Engineering Efficiency

My journey to understanding Price’s Law began with a fascination for the Antikythera Mechanism —an ancient Greek marvel of engineering and astronomy. It was through this mechanism that I first encountered the work of Prof. Derek J. de Solla Price, a British physicist and historian whose curiosity and intellect extended far beyond antiquities. Inspired by the ingenuity of the Antikythera Mechanism, I was drawn to explore the origins of Damascus and Wootz steel, and its roots in the south-western peninsula of India (as detailed in Aayutha Desam by R. Mannar Mannan). (More about that in another post!)

But it was Price’s insight into the uneven distribution of productivity in groups that struck a chord with my work in software engineering. His principle, now widely known as Price’s Law, asserts that in any team, 50% of the work is accomplished by the square root of the total number of participants.

  • In a team of 10 engineers, approximately 3 contributors (√10) are responsible for half the output.
  • In a team of 100 engineers, only 10 individuals (√100) produce as much as the remaining 90 combined.

This principle highlights a counterintuitive but vital truth: as team size grows, the proportion of high contributors decreases, leading to inefficiencies that compound over time. This isn’t just an academic curiosity—it’s a critical insight for engineering leaders tasked with scaling teams and delivering results.

Price’s Law challenges a long-standing assumption in engineering leadership: that scaling teams proportionally scales productivity. By understanding this principle, CTOs, VPs, and engineering managers can rethink strategies for achieving efficiency and delivering value, even with constrained resources.

The Myth of Highly Motivated Teams

Some self-proclaimed visionary leaders advocate for hiring only highly motivated individuals, often overlooking how teams function in practice. In any organized group, work typically falls into three categories:

  1. Drudgery Work (Low Impact, High Intensity): Routine tasks like debugging or documentation, essential but unappealing.
  2. Intermediate Work (Medium Impact, Medium Intensity): Feature upgrades or system integrations, vital for sustaining operations.
  3. Challenging Work (High Impact, High Intensity): Complex, high-stakes initiatives that highly motivated individuals prefer.

The Problem

Highly motivated individuals often prioritize high-impact projects, leaving routine and intermediate work neglected. This creates:

  • Operational Bottlenecks: Accumulating technical debt and system fragility.
  • Imbalanced Workloads: Overburdened team members handling routine tasks.
  • Team Friction: Reduced cohesion and potential burnout.

The Solution: Balance Over Ambition

Effective teams thrive on diversity in skill sets and balanced task allocation. Leaders must:

  • Distribute Work Strategically: Ensure all types of work are addressed.
  • Value Contributions Equally: Recognize the importance of routine and intermediate tasks.
  • Foster Team Cohesion: Avoid over-prioritizing high-stakes projects at the expense of operational stability.

Conclusion: A truly visionary leader grounds ambition in pragmatism, creating teams that excel not just in high-impact projects but also in sustaining the essentials of day-to-day operations.

Implications for Team Expansion

For CTOs, VPs, and engineering managers, this dynamic presents a counterintuitive challenge: merely expanding the team does not guarantee proportional gains in productivity. Doubling headcount often introduces:

  1. Communication Overhead: Larger teams require more coordination, which consumes valuable time and resources.
  2. Dilution of Accountability: As teams grow, individual contributions become harder to track, potentially reducing ownership and engagement.
  3. Coordination Complexities: Increased interdependencies among team members can slow down decision-making and implementation.

To achieve a twofold increase in productivity, Price’s Law suggests that you may need to quadruple the team size, a move that is often impractical and financially untenable. Instead, engineering leaders must rethink productivity beyond the simplistic metric of team size.

Shifting Focus: Outcomes Over Outputs

Traditional productivity metrics, such as the number of features released or lines of code written, focus on outputs—tangible deliverables produced by the team. However, outputs do not inherently translate into value. Consider the distinction:

  • Outputs: Metrics like features delivered or tickets closed.
  • Outcomes: Measurable changes in user behaviour that drive business results, such as increased user retention or reduced churn.

Relying solely on outputs creates a misleading picture of productivity. A feature-rich application that fails to address user needs or business goals is ultimately unproductive. Instead, outcomes—which capture the real-world effectiveness of engineering efforts—offer a better lens to measure success.

Outcome vs. Impact

While outcomes focus on immediate effects (e.g., increased sign-ups from a new feature), impact delves deeper into long-term consequences. For example:

  • An outcome may be an increase in user sign-ups after a feature launch.
  • The impact would be sustained revenue growth and user satisfaction resulting from the feature’s value over time.

Engineering teams must aim for outcomes that align with strategic goals while keeping an eye on their long-term impacts.

Counterproductive Paradigm: The Threat Surface of Excessive Outputs

Emphasizing outputs over outcomes can be counterproductive, leading to what can be described as an expanding threat surface:

  1. Defects and Bugs: Adding more features often introduce unintended issues that require additional resources to resolve.
  2. Maintenance Burden: More code increases the risk of technical debt, making future development slower and more complex.
  3. Conflict Resolution: Larger teams fixing bugs or implementing features in parallel can inadvertently cause regressions, especially when the main sprint continues uninterrupted.

This vicious cycle diverts focus from strategic initiatives, tying up engineers in a continuous loop of fixes. Instead of scaling output indiscriminately, teams should focus on ensuring that every deliverable contributes to meaningful outcomes.

Focusing on Impacts and Outcomes: A Leadership Imperative

For engineering leaders, the shift from outputs to impacts and outcomes is transformative. This approach emphasizes:

  1. Defining Clear Objectives: Establish measurable outcomes (e.g., reducing churn by 10%) that align with business goals.
  2. Prioritizing High-Impact Work: Evaluate tasks based on their potential to deliver meaningful results.
  3. Empowering Teams: Foster a culture where engineers understand and contribute to broader business objectives rather than just completing tickets.
  4. Continuous Feedback Loops: Regularly assess whether engineering efforts are driving intended outcomes.

This shift not only enhances productivity but also aligns engineering work with the organization’s mission, fostering a sense of purpose within teams.

Conclusion: Redefining Productivity in Software Engineering

Price’s Law reminds us that productivity does not scale linearly with team size. Engineering leaders must navigate this reality by focusing on outcomes and impacts rather than outputs. This paradigm shift requires a cultural and strategic overhaul, but the rewards—greater efficiency, alignment, and value delivery—are well worth the effort.

By embracing this approach, organizations can ensure that their engineering efforts contribute directly to their strategic goals, transforming software development into a driver of sustainable business success.

References

  1. Sundarakalatharan, R. (2022). How to measure Engineering Productivity?. Retrieved from https://nocturnalknight.co/how-to-measure-engineering-productivity/
  2. Bohrmann, N. (2022). How Price’s Law Applies to Everything. Retrieved from https://nielsbohrmann.com/prices-law/
  3. LeadDev. (2022). Focus on outcomes over outputs. Retrieved from https://leaddev.com/velocity/focus-outcomes-over-outputs
  4. Monday Mornings. (2023). Productivity and Price’s Law. Retrieved from https://mondaymornings.madisoncres.com/productivity-and-prices-law-1
  5. TechRadar. (2023). Outcomes versus outputs: the real measure of developer productivity. Retrieved from https://www.techradar.com/pro/outcomes-versus-outputs-the-real-measure-of-developer-productivity
  6. Royal Holloway Information Security Group. (2024). https://pure.royalholloway.ac.uk/
  7. Wikipedia. (2024). Antikythera Mechanism. Retrieved from https://en.wikipedia.org/wiki/Antikythera_mechanism
  8. Wikipedia. (2024). Derek J. de Solla Price. Retrieved from https://en.wikipedia.org/wiki/Derek_J._de_Solla_Price
  9. Wikipedia. (2024). Wootz Steel. Retrieved from https://en.wikipedia.org/wiki/Wootz_steel
  10. Purple Book House. (2024). Aayutha Desam by R. Mannar Mannan. Retrieved from https://www.purplebookhouse.co.uk/product-page/aayutha-desam-book-type-katturaigal-history-by-r-mannar-mannan
The Truth About “Ghost Engineers”: A Critical Analysis

The Truth About “Ghost Engineers”: A Critical Analysis

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Disclaimer:
This article is not intended to discredit Boris Denisov, Stanford University, McKinsey, or any other entities referenced herein. I hold immense respect for their contributions to research and industry discourse. While findings like these may resonate with practices in FAANG companies, large organizations, and mature startups, this critique seeks to explore the broader implications of relying on narrow metrics to evaluate productivity in software engineering.

The “Ghost Engineer” Narrative

The term “ghost engineers,” popularized by a recent Stanford study, describes software engineers who allegedly contribute minimally to codebases. Analyzing data from over 50,000 engineers, the study concludes that 9.5% of engineers fall into this category, with the prevalence rising to 14% among remote workers​.

While the findings spark interesting discussions, they rely heavily on the flawed assumption that code commit frequency equates to productivity. As I argued in No, McKinsey, You Got It All Wrong About Developer Productivity, this narrow perspective risks undervaluing critical aspects of software engineering that don’t leave a visible footprint in version control systems​​.

Unintended Amplification: The Snowball Effect

One of the most significant risks of such conclusions—especially before peer review—is their unintended amplification. Articles on Yahoo, TechCrunch, and Newsday have already simplified these findings, creating narratives that could ripple through the industry:

  1. Unnecessary Layoffs: Misinterpreting data might lead organizations to hastily classify engineers as unproductive, ignoring less visible but valuable contributions.
  2. Remote Work Stigma: By associating remote work with reduced productivity, these claims risk undermining one of the most effective workforce models when well-managed.
  3. Toxic Metrics Culture: Over-reliance on activity metrics like commit counts can encourage engineers to game the system by prioritizing volume over meaningful work, as discussed in Business Value Delivery by Engineering Teams in Startups (Part 2)​.

History offers cautionary examples, such as McKinsey’s controversial reliance on lines of code as a productivity measure—a practice criticized in my earlier article for ignoring the multifaceted nature of modern software engineering​​.

Engineering Productivity: Beyond Output Metrics

As outlined in Is the Myth of a 10x Developer Real?, productivity in software engineering extends far beyond raw output. Effective engineers don’t just code—they align stakeholders, resolve ambiguity, and reduce future risks. These invisible contributions often lead to:

  • Improved Collaboration: Engineers who mentor, review code, or resolve cross-team dependencies amplify the impact of their teams.
  • Strategic Outcomes: Refactoring technical debt or implementing security frameworks might reduce visible code output while significantly improving system health​​.

Commit Frequency Misses Critical Context

  • Quality Over Quantity: A single commit that eliminates 1,000 lines of redundant code can be more impactful than 10 minor feature updates.
  • Diverse Roles: Roles like DevOps, QA, and security often contribute indirectly to engineering success but rarely generate frequent commits.

By focusing solely on visible metrics, we risk reinforcing flawed incentives, a point I emphasized in Business Value Delivery by Engineering Teams in Startups (Part 1)​​.

Analyzing the Stanford Study’s Claims

Claim 1: Engineers with Low Commit Activity Are Unproductive

Rebuttal: This assumption ignores the cognitive and collaborative aspects of engineering. As noted in No, McKinsey, You Got It All Wrong About Developer Productivity, activities like design discussions, documentation, and mentoring are essential but invisible in commit logs​.

Claim 2: Remote Engineers Are More Likely to Be “Ghost Engineers”

Rebuttal: Remote work relies on asynchronous collaboration, where documentation and long-term planning take precedence over immediate outputs. Simplistic comparisons risk stigmatizing effective remote models​​.

Claim 3: Low Commit Activity Correlates with Poor Team Performance

Rebuttal: High-performing teams often include specialists whose contributions are less visible but critical. For example, a security engineer resolving vulnerabilities or a DevOps engineer optimizing CI/CD pipelines may not show up in commit logs​.

Claim 4: Organizations Could Save Billions by Addressing the “Ghost Engineer” Problem

Rebuttal: Cost-cutting measures based on flawed metrics often lead to higher technical debt, increased turnover, and diminished morale. As argued in Business Value Delivery by Engineering Teams in Startups (Part 2), true cost efficiency lies in maximizing impact, not minimizing headcount​.

Impact vs Code-Commits: Understanding the Misalignment

A recurring issue with productivity metrics like code-commit frequency is their inability to reflect the true impact of an engineer’s work. The volume of code changes often says little about the value delivered, as demonstrated by the following examples:

Example 1: A Cosmetic UI Change vs. A Critical API Update

Imagine a product manager requests a seemingly simple change: update a button’s color from purple to orange. While this may sound trivial, it could involve:

  • Updating CSS libraries: A cascade of dependencies might require 1,000+ lines of revisions.
  • Testing for accessibility: Ensuring compliance with color-contrast guidelines adds complexity.
  • Regression testing: Updating snapshot tests or fixing broken visual diffs.

This cosmetic change could result in dozens of commits, each addressing a specific dependency or edge case.

Contrast this with a backend engineer’s work on the API gateway to improve application concurrency. This might involve:

  • Identifying bottlenecks: Profiling existing workloads and implementing a solution to reduce latency.
  • Optimizing database connections: Reducing round trips or improving query performance.
  • Deploying with minimal disruption: A single, concise commit could encapsulate weeks of planning and testing.

Here, the backend change’s impact far outweighs the UI update, even though it appears smaller in terms of commit frequency.

Example 2: Bulk Refactoring vs. Precise Bug Fixing

A mid-level engineer is tasked with refactoring a legacy module, updating deprecated methods, and restructuring a monolithic codebase for better readability. This effort generates hundreds of commits and thousands of lines of changes, none of which immediately improve the product’s features.

On the other hand, a senior engineer identifies and fixes a critical bug that intermittently crashes the application. The solution, a one-line code change after hours of debugging, resolves a high-severity issue affecting thousands of users.

From a commit-count perspective, the refactoring task appears more productive. However, the senior engineer’s single-line fix has a far greater immediate impact.

Example 3: Feature Addition vs. Security Enhancement

A frontend developer introduces a new feature, such as a user profile editor. This entails:

  • New UI components: HTML and CSS for the form.
  • Frontend validations: JavaScript-based constraints for data inputs.
  • Integration tests: Mock API responses for various test cases.

The addition spans 2,000 lines of code across 20 commits.

Meanwhile, a DevSecOps engineer works on a critical security vulnerability. The task involves:

  • Rotating access tokens: Updating key secrets stored in the CI/CD pipeline.
  • Implementing security headers: Adding CSPs to prevent XSS attacks.
  • Hardening configurations: Minor changes in deployment scripts to reduce attack surfaces.

Although the security enhancement generates fewer than 10 commits, its value in preventing potential breaches and compliance penalties is enormous.

Key Takeaways

  • Context Matters: Evaluating productivity requires understanding the context and complexity of the task, not just the output volume.
  • Quality Over Quantity: High-impact changes often involve fewer commits, while low-value tasks may inflate commit counts.
  • Recognizing Diverse Contributions: Engineers working on performance, security, or architecture frequently produce less visible yet highly impactful work.

This misalignment underscores the need for organizations to adopt holistic evaluation metrics that consider both quantitative output and qualitative impact. By focusing on the latter, teams can better recognize and reward meaningful contributions.

The Danger of Flawed Productivity Metrics

Simplistic metrics can have cascading negative effects:

  1. Burnout: Engineers may feel pressured to prioritize activity over quality.
  2. Stifled Innovation: Overemphasis on visible output discourages experimentation and risk-taking.
  3. Loss of Talent: Talented engineers in specialized roles may leave if their contributions are undervalued.

As emphasized in Is the Myth of a 10x Developer Real?, effective engineering is about multiplying impact, not maximizing visible output​​.

A Holistic Approach to Productivity

To address these issues, organizations must adopt nuanced evaluation frameworks:

  1. Impact-Driven Metrics: Evaluate contributions based on outcomes, such as improved system reliability or customer satisfaction.
  2. Recognize Invisible Work: Acknowledge tasks like mentorship, technical debt reduction, and long-term strategic planning.
  3. Foster a Culture of Trust: Empower teams to experiment and innovate without fear of being misjudged by flawed metrics.

Conclusion

The “ghost engineer” narrative oversimplifies the multifaceted nature of software engineering. By relying on metrics like commit counts, it risks undervaluing critical contributions and fostering unhealthy workplace dynamics. As I’ve argued across multiple articles, effective engineering teams succeed by delivering value, not just output. The industry must move beyond flawed productivity metrics and adopt more comprehensive frameworks to recognize the true contributions of every engineer.

References and Further Reading

  1. Denisov-Blanch, Y. (2024). Twitter Thread on Ghost Engineers. Retrieved from link.
  2. Denisov-Blanch, Y. (2024). Stanford Research on Software Engineering Productivity. Stanford University. Retrieved from link.
  3. Polyakov, A. (2024). Ghost Engineers—Utter Non-Sense! Medium. Retrieved from link.
  4. No, McKinsey, You Got It All Wrong About Developer Productivity. Nocturnalknight.co. Retrieved from link.
  5. Is the Myth of a 10x Developer Real? Nocturnalknight.co. Retrieved from link.
  6. Bridgwater, A. (2024). Code Busters: Are Ghost Engineers Haunting DevOps Productivity? DevOps.com. Retrieved from link.
  7. Business Value Delivery by Engineering Teams in Startups (Part 1). Nocturnalknight.co. Retrieved from link.
  8. Business Value Delivery by Engineering Teams in Startups (Part 2). Nocturnalknight.co. Retrieved from link.
  9. Long, K. (2024). Are Ghost Engineers Undermining Tech Productivity? Business Insider. Retrieved from link.
  10. Passionate Geekz. (2024). Can a Company Increase Its Market Value by Laying Off Employees? Retrieved from link.
Do You Know What’s in Your Supply Chain? The Case for Better Security

Do You Know What’s in Your Supply Chain? The Case for Better Security

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I recently read an interesting report by CyCognito on the top 3 vulnerabilities on third-party products and it sparked my interest to reexamine the supply chain risks in software engineering. This article is an attempt at that.

The Vulnerability Trifecta in Third-Party Products

The CyCognito report identifies three critical areas where third-party products introduce significant vulnerabilities:

  1. Web Servers
    These foundational systems host countless applications but are frequently exploited due to misconfigurations or outdated software. According to the report, 34% of severe security issues are tied to web server environments like Apache, NGINX, and Microsoft IIS. Vulnerabilities like directory traversal or improper access control can serve as gateways for attackers.
  2. Cryptographic Protocols
    Secure communication relies on cryptographic protocols like TLS and HTTPS. Yet, 15% of severe vulnerabilities target these mechanisms. For instance, misconfigurations, weak ciphers, or reliance on deprecated standards expose sensitive data, with inadequate encryption ranking second on OWASP’s Top 10 security threats.
  3. Web Interfaces Handling PII
    Applications that process PII—such as invoices or financial statements—are among the most sensitive assets. Alarmingly, only half of such interfaces are protected by Web Application Firewalls (WAFs), leaving them vulnerable to injection attacks, session hijacking, or data leakage.

Beyond Web Servers: The Hidden Dependency Risks

You control your software stack, but do you actually know what runs beneath those flashy Web/Application servers?

Drawing parallels from my previous article on PyPI and NPM vulnerabilities, it’s clear that open-source dependencies amplify these threats. Attackers exploit the very trust inherent in supply chains, introducing malicious packages or exploiting insecure libraries.

For example:

  • Attackers have embedded malware into popular NPM and PyPI packages, which are then unknowingly incorporated into enterprise-grade software.
  • Dependency confusion attacks exploit naming conventions to inject malicious packages into CI/CD pipelines.

These risks share a core vulnerability with traditional third-party systems: an opaque supply chain with minimal oversight. This is compounded by the ever-decreasing cycle-times for each software releases, giving little to no time for even great Software Engineering teams to doa decent audit and look into the dependency graph of the packages they are building their new, shiny/pointy things that is to transform the world.

Why Software Supply Chain Attacks Persist

As highlighted by Scientific Computing World, software supply chain attacks persist for several reasons:

  • Aggressive GTM Timelines: Most organisations now run quarterly or even monthly product roadmaps, so it is possible to launch a new SaaS product in a matter of days to weeks by leveraging other IaaS, PaaS or SaaS systems – in addition to any Libraries, frameworks and other constructs.
  • Exponential Complexity: With organisations relying on layers of third-party and fourth-party services, the attack surface expands exponentially.
  • Insufficient Oversight: Organisations often focus on securing their environments while neglecting the vendors and libraries they depend on.
  • Lagging Standards: The industry’s inability to enforce stringent security protocols across the supply chain leaves critical gaps.
  • Sophistication of Attacks: From SolarWinds to MOVEit, attackers continually evolve, targeting blind spots in detection and remediation frameworks.

Recommended Steps to Mitigate Supply Chain Threats

To address these vulnerabilities and build resilience, organizations can take the following actionable steps:

1. Map and Assess Dependencies

  • Use tools like Dependency-Track or Sonatype Nexus to map and analyze all third-party and open-source dependencies.
  • Regularly perform software composition analysis (SCA) to detect outdated or vulnerable components.

2. Implement Zero-Trust Architecture

  • Leverage Zero-Trust frameworks like NIST 800-207 to ensure strict authentication and access controls across all systems.
  • Minimize the privileges of third-party integrations and isolate sensitive data wherever possible.

3. Strengthen Vendor Management

  • Evaluate vendor security practices using frameworks like the NCSC’s Supply Chain Security Principles or the Open Trusted Technology Provider Standard (OTTPS).
  • Demand transparency through detailed Service Level Agreements (SLAs) and regular vendor audits.

4. Prioritize Secure Development and Deployment

  • Train your development teams to follow secure coding practices like those outlined in the OWASP Secure Coding Guidelines.
  • Incorporate tools like Snyk or Checkmarx to identify vulnerabilities during the software development lifecycle.

5. Enhance Monitoring and Incident Response

  • Deploy Web Application Firewalls (WAFs) such as AWS WAF or Cloudflare to protect web interfaces.
  • Establish a robust incident response plan using guidance from the MITRE ATT&CK Framework to ensure rapid containment and mitigation.

6. Foster Collaboration

  • Work with industry peers and organizations like the Cybersecurity and Infrastructure Security Agency (CISA) to share intelligence and best practices for supply chain security.
  • Collaborate with academic institutions and research groups for cutting-edge insights into emerging threats.

7. Schedule a No-Obligation Consultation Call with Yours Truly

Struggling with supply chain vulnerabilities or need tailored solutions for your unique challenges? I offer consultation services to work directly with your CTO, Principal Architect, or Security Leadership team to:

  • Assess your systems and identify key risks.
  • Recommend actionable, budget-friendly steps for mitigation and prevention.

With years of expertise in cybersecurity and compliance, I can help streamline your approach to supply chain security without breaking the bank. Let’s collaborate to make your operations secure and resilient.

Schedule Your Free Consultation Today

Building a Resilient Supply Chain

The UK’s National Cyber Security Centre (NCSC) principles for supply chain security provide a pragmatic roadmap for businesses. Here’s how to act:

  1. Understand and Map Dependencies
    Organizations should create a detailed map of all dependencies, including direct vendors and downstream providers, to identify potential weak links.
  2. Adopt a Zero-Trust Framework
    Treat every external connection as untrusted until verified, with continuous monitoring and access restrictions.
  3. Mandate Secure Development Practices
    Encourage or require vendors to implement secure coding standards, frequent vulnerability testing, and robust update mechanisms.
  4. Regularly Audit Supply Chains
    Establish a routine audit process to assess vendor security posture and adherence to compliance requirements.
  5. Proactive Incident Response Planning
    Prepare for the inevitable by maintaining a robust incident response plan that incorporates supply chain risks.

Final Thoughts

The threat of supply chain vulnerabilities is no longer hypothetical—it’s happening now. With reports like CyCognito’s, research into dependency management, and frameworks provided by trusted institutions, businesses have the tools to mitigate risks. However, this requires vigilance, collaboration, and a willingness to rethink traditional approaches to third-party management.

Organisations must act not only to safeguard their operations but also to preserve trust in an increasingly interconnected world. 

Is your supply chain ready to withstand the next wave of attacks?

References and Further Reading

  1. Report Shows the Threat of Supply Chain Vulnerabilities from Third-Party Products – CyCognito
  2. Hidden Threats in PyPI and NPM: What You Need to Know
  3. Why Software Supply Chain Attacks Persist – Scientific Computing World
  4. Principles of Supply Chain Security – NCSC
  5. CyCognito Report Exposes Rising Software Supply Chain Threats

What’s your strategy for managing third-party risks? Share your thoughts in the comments!

Why Do We Need Quantum-Resistant Security Standards?

Why Do We Need Quantum-Resistant Security Standards?

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In October 2024, we discussed the profound implications of China’s quantum computing advancements and their potential to disrupt internet security. Quantum computers, with their unparalleled processing power, pose a direct threat to current encryption systems that secure global communications. Since then, the National Institute of Standards and Technology (NIST) has made significant strides in shaping the post-quantum cryptography (PQC) landscape. This follow-up delves into NIST’s recent updates, including finalised standards, transition strategies, and their broader impact on global cybersecurity.

NIST’s Finalised Post-Quantum Encryption Standards

On August 13, 2024, NIST announced the release of its first three finalized post-quantum encryption standards. These standards are foundational for safeguarding electronic information in a quantum-enabled future, addressing key areas such as secure email communications, online transactions, and identity verification.

The standards selected are robust against both classical and quantum attacks, offering a proactive defence against the anticipated rise of quantum threats. While these are groundbreaking, NIST has emphasized the need for rapid adoption, encouraging enterprises and governments alike to begin transitioning their systems to quantum-resistant encryption.

Key highlights:

  • Algorithms: CRYSTALS-Kyber (public key encryption) and CRYSTALS-Dilithium (digital signatures) lead the finalized standards.
  • Applications: These standards are particularly suited for critical applications, such as financial systems, healthcare records, and government communications.

NIST’s Draft Transition Strategy and Timeline

In a draft report released on November 14, 2024, NIST outlined a detailed roadmap for migrating to PQC. This document provides clarity on the timeline and steps necessary to shift from current cryptographic protocols to quantum-resistant ones.

Key Aspects of the Draft:

  1. Transition Timeline:
    • Transition to begin immediately, with milestones for algorithm implementation by 2026.
    • Full adoption in federal systems is targeted by 2030, though enterprises are urged to act sooner.
  2. Evaluation and Risk Management:
    • A phased approach to identify and replace quantum-vulnerable systems.
    • Focus on testing and interoperability with existing infrastructure.
  3. Public Review Period:
    • The draft is open for comments until January 10, 2025, ensuring that the strategy incorporates diverse perspectives from industry leaders, academia, and government.

Guidance for Federal Agencies and Enterprises

To aid the transition, NIST has issued specific guidance tailored for federal agencies and private organizations:

  • Quantum Risk Assessments: Organizations must inventory their cryptographic systems and identify components vulnerable to quantum decryption.
  • Pilot Programs: Encouraged for testing quantum-resistant algorithms in controlled environments.
  • Training and Awareness: Enterprises need to upskill their workforce to understand and implement PQC effectively.

This proactive approach aligns with Executive Order 14028 on improving national cybersecurity, which mandates the adoption of innovative security measures across federal systems.

Enterprises Must Act Faster

While NIST has provided a structured timeline, cybersecurity experts warn that enterprises cannot afford to wait until the final deadlines. The development of practical quantum computers may outpace current expectations, leaving vulnerable systems exposed.

Recommendations for Enterprises:

  1. Prioritise Cryptographic Inventories: Develop a clear understanding of where cryptography is used and its quantum vulnerability.
  2. Develop a Migration Plan: Incorporate NIST’s guidance to create a tailored transition strategy.
  3. Collaborate with Vendors: Work with software and hardware providers to ensure seamless updates and integrations of PQC algorithms.

Global Implications and Call to Action

The transition to PQC is not just a technical challenge but a global imperative. With quantum computing breakthroughs occurring across nations, adopting quantum-resistant standards is essential for maintaining the integrity of digital systems. Organizations worldwide must:

  • Collaborate to ensure interoperability of PQC standards across borders.
  • Share best practices and innovations to accelerate the global transition.
  • Support research in next-generation cryptographic techniques to stay ahead of emerging threats.

Conclusion

NIST’s efforts in finalizing post-quantum encryption standards and drafting a comprehensive transition strategy mark a pivotal moment in cybersecurity. However, these initiatives are only as effective as their adoption. Governments, enterprises, and individuals must take urgent steps to align with these standards and safeguard their digital assets against the looming threat of quantum-powered attacks.

For further insights into how quantum computing advancements could reshape internet security, revisit our previous discussion: How Will China’s Quantum Advances Change Internet Security?.

References & Further Reading: 

  1. NIST IR 8547 – https://csrc.nist.gov/pubs/ir/8547/ipd
  2. NIST IR 8413 – https://nvlpubs.nist.gov/nistpubs/ir/2022/NIST.IR.8413.pdf
  3. Dilithium – https://pq-crystals.org/dilithium/
  4. Falcon – https://falcon-sign.info/
  5. PHINCS+ – https://sphincs.org/ 
  6. Trapdoor for hard Lattices in Cryptographic Constructs – https://eprint.iacr.org/2007/432 (Must read if you’re a programmer and interested in exploring Lattices) 
  7. Lattice-based cryptography – Chris Peikert, Georgia Institute of Tech – https://web.eecs.umich.edu/~cpeikert/pubs/slides-abit4.pdf
  8. Additional Source Codes to Explore – https://github.com/regras/labs  (This project is a Proof of Concept (PoC), about an Attribute-Based Signature scheme using lattices.)
Hidden Threats in PyPI and NPM: What You Need to Know

Hidden Threats in PyPI and NPM: What You Need to Know

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Introduction: Dependency Dangers in the Developer Ecosystem

Modern software development is fuelled by open-source packages, ranging from Python (PyPI) and JavaScript (npm) to PHP (phar) and pip modules. These packages have revolutionised development cycles by providing reusable components, thereby accelerating productivity and creating a rich ecosystem for innovation. However, this very reliance comes with a significant security risk: these widely used packages have become an attractive target for cybercriminals. As developers seek to expedite the development process, they may overlook the necessary due diligence on third-party packages, opening the door to potential security breaches.

Faster Development, Shorter Diligence: A Security Conundrum

Today, shorter development cycles and agile methodologies demand speed and flexibility. Continuous Integration/Continuous Deployment (CI/CD) pipelines encourage rapid iterations and frequent releases, leaving little time for the verification of every dependency. The result? Developers often choose dependencies without conducting rigorous checks on package integrity or legitimacy. This environment creates an opening for attackers to distribute malicious packages by leveraging popular repositories such as PyPI, npm, and others, making them vectors for harmful payloads and information theft.

Malicious Package Techniques: A Deeper Dive

While typosquatting is a common technique used by attackers, there are several other methods employed to distribute malicious packages:

  • Supply Chain Attacks: Attackers compromise legitimate packages by gaining access to the repository or the maintainer’s account. Once access is obtained, they inject malicious code into trusted packages, which then get distributed to unsuspecting users.
  • Dependency Confusion: This technique involves uploading packages with names identical to internal, private dependencies used by companies. When developers inadvertently pull from the public repository instead of their internal one, they introduce malicious code into their projects. This method exploits the default behaviour of package managers prioritising public over private packages.
  • Malicious Code Injection: Attackers often inject harmful scripts directly into a package’s source code. This can be done by compromising a developer’s environment or using compromised libraries as dependencies, allowing attackers to spread the malicious payload to all users of that package.

These methods are increasingly sophisticated, leveraging the natural behaviours of developers and package management systems to spread malicious code, steal sensitive information, or compromise entire systems.

Timeline of Incidents: Malicious Packages in the Spotlight

A series of high-profile incidents have demonstrated the vulnerabilities inherent in unchecked package installations:

  • June 2022: Malicious Python packages such as loglib-modules, pyg-modules, pygrata, pygrata-utils, and hkg-sol-utils were caught exfiltrating AWS credentials and sensitive developer information to unsecured endpoints. These packages were disguised to look like legitimate tools and fooled many unsuspecting developers. (BleepingComputer)
  • December 2022: A malicious package masquerading as a SentinelOne SDK was uploaded to PyPI, with malware designed to exfiltrate sensitive data from infected systems. (The Register)
  • January 2023: The popular ctx package was compromised to steal environment variables, including AWS keys, and send them to a remote server. This instance affected many developers and highlighted the scale of potential data leakage through dependencies. (BleepingComputer)
  • September 2023: An extended campaign involving malicious npm and PyPI packages targeted developers to steal SSH keys, AWS credentials, and other sensitive information, affecting numerous projects globally. (BleepingComputer)
  • October 2023: The recent incident involving the fabrice package is a stark reminder of how easy it is for attackers to deceive developers. The fabrice package, designed to mimic the legitimate fabric library, employed a typosquatting strategy, exploiting typographical errors to infiltrate systems. Since its release, the package was downloaded over 37,000 times and covertly collected AWS credentials using the boto3 library, transmitting the stolen data to a remote server via VPN, thereby obscuring the true origin of the attack. The package contained different payloads for Linux and Windows systems, utilising scheduled tasks and hidden directories to establish persistence. (Developer-Tech)

The Impact: Scope of Compromise

The estimated number of affected companies and products is difficult to pin down precisely due to the widespread usage of open-source packages in both small-scale and enterprise-level applications. Given that some of these malicious packages garnered tens of thousands of downloads, the potential damage stretches across countless software projects. With popular packages like ctx and others reaching a substantial audience, the economic and reputational impact could be significant, potentially costing affected businesses millions in breach recovery and remediation costs.

Real-world Impact: Consequences of Malicious Packages

The real-world impact of malicious packages is profound, with consequences ranging from data breaches to financial loss and severe reputational damage. The following are some of the key impacts:

  • British Airways and Ticketmaster Data Breach: In 2018, the Magecart group exploited vulnerabilities in third-party scripts used by British Airways and Ticketmaster. The attackers injected malicious code to skim payment details of customers, leading to significant data breaches and financial loss. British Airways was fined £20 million for the breach, which affected over 400,000 customers. (BBC)
  • Codecov Bash Uploader Incident: In April 2021, Codecov, a popular code coverage tool, was compromised. Attackers modified the Bash Uploader script, which is used to send coverage reports, to collect sensitive information from Codecov’s users, including credentials, tokens, and keys. This supply chain attack impacted hundreds of customers, including notable companies like HashiCorp. (GitGuardian)
  • Event-Stream NPM Package Attack: In 2018, a popular JavaScript library event-stream was hijacked by a malicious actor who added code to steal cryptocurrency from applications using the library. The compromised version was downloaded millions of times before the attack was detected, affecting numerous developers and projects globally. (Synk)

These incidents highlight the potential repercussions of malicious packages, including severe financial penalties, reputational damage, and the theft of sensitive customer information.

Fabrice: A Case Study in Typosquatting

The recent incident involving the fabrice package is a stark reminder of how easy it is for attackers to deceive developers. The fabrice package, designed to mimic the legitimate fabric library, employed a typosquatting strategy, exploiting typographical errors to infiltrate systems. Since its release, the package was downloaded over 37,000 times and covertly collected AWS credentials using the boto3 library, transmitting the stolen data to a remote server via VPN, thereby obscuring the true origin of the attack. The package contained different payloads for Linux and Windows systems, utilising scheduled tasks and hidden directories to establish persistence. (Developer-Tech)

Lessons Learned: Importance of Proactive Security Measures

The cases highlighted in this article offer important lessons for developers and organisations:

  1. Dependency Verification is Crucial: Typosquatting and dependency confusion can be avoided by carefully verifying package authenticity. Implementing strict naming conventions and utilising internal package repositories can help prevent these attacks.
  2. Security Throughout the SDLC: Integrating security checks into every phase of the SDLC, including automated code reviews and security testing of modules, is essential. This ensures that vulnerabilities are identified early and mitigated before reaching production.
  3. Use of Vulnerability Scanning Tools: Tools like Snyk and OWASP Dependency-Check are invaluable in proactively identifying vulnerabilities. Organisations should make these tools a mandatory part of the development process to mitigate risks from third-party dependencies.
  4. Security Training and Awareness: Developers must be educated about the risks associated with third-party packages and taught how to identify potentially malicious code. Regular training can significantly reduce the likelihood of falling victim to these attacks.

By recognising these lessons, developers and organisations can better safeguard their software supply chains and mitigate the risks associated with third-party dependencies.

Prevention Strategies: Staying Safe from Malicious Packages

To mitigate the risks associated with malicious packages, developers and startups must adopt a multi-layered defence approach:

  1. Verify Package Authenticity: Always verify package names, descriptions, and maintainers. Opt for well-reviewed and frequently updated packages over relatively unknown ones.
  2. Review Source Code: Whenever possible, review the source code of the package, especially for dependencies with recent uploads or unknown maintainers.
  3. Use Package Scanners: Employ tools like Sonatype Nexus, npm audit, or PyUp to identify vulnerabilities and malicious code within packages.
  4. Leverage Lockfiles: Tools like package-lock.json (npm) or Pipfile.lock (pip) can help prevent unintended updates by locking dependencies to a specific version.
  5. Implement Least Privilege: Limit the permissions assigned to development environments to reduce the impact of compromised keys or credentials.
  6. Regular Audits: Conduct regular security audits of dependencies as part of the CI/CD pipeline to minimise risk.

Software Security: Embedding Security in the Development Lifecycle

To mitigate the risks associated with malicious packages and other vulnerabilities, it is essential to integrate security into every phase of the Software Development Lifecycle (SDLC). This practice, known as the Secure Software Development Lifecycle (SSDLC), emphasises incorporating security best practices throughout the development process.

Key Components of SSDLC

  • Automated Code Reviews: Leveraging tools that automatically scan code for vulnerabilities and flag potential issues early in the development cycle can significantly reduce the risk of security flaws making it into production. Tools like SonarQube, Checkmarx, and Veracode help in ensuring that security is built into the code from the beginning.
  • Security Testing of Modules: Security testing should be conducted on third-party modules before integrating them into the project. Tools like Snyk and OWASP Dependency-Check can identify vulnerabilities in dependencies and provide remediation advice.

Deep Dive into Technical Details

  • Malicious Package Techniques: As discussed earlier, typosquatting is just one of the many attack techniques. Supply chain attacks, dependency confusion, and malicious code injection are also common methods attackers use to compromise software projects. It is essential to understand these techniques and incorporate checks that can prevent such attacks during the development process.
  • Vulnerability Analysis Tools:
    • Snyk: Snyk helps developers identify vulnerabilities in open-source libraries and container images. It scans the project dependencies and cross-references them with a constantly updated vulnerability database. Once vulnerabilities are identified, Snyk provides detailed remediation advice, including fixing the version or applying patches.
    • OWASP Dependency-Check: OWASP Dependency-Check is an open-source tool that scans project dependencies for known vulnerabilities. It works by identifying the libraries used in the project, then checking them against the National Vulnerability Database (NVD) to highlight potential risks. The tool also provides reports and actionable insights to help developers remediate the issues.
    • Sonatype Nexus: Sonatype Nexus offers a repository management system that integrates directly with CI/CD pipelines to scan for vulnerabilities. It uses machine learning and other advanced techniques to continuously monitor and evaluate open-source libraries, providing alerts and remediation options.

Best Practices for Secure Dependency Management

  • Dependency Pinning: Pinning dependencies to specific versions helps in preventing unexpected updates that may contain vulnerabilities. By using tools like package-lock.json (npm) or Pipfile.lock (pip), developers can ensure that they are not inadvertently upgrading to a compromised version of a dependency.
  • Use of Private Registries: Hosting private package registries allows organisations to maintain tighter control over the dependencies used in their projects. By using tools like Nexus Repository or Artifactory, companies can create a trusted repository of dependencies and mitigate risks associated with public registries.
  • Robust Security Policies: Organisations should implement strict policies around the use of open-source components. This includes performing security audits, using automated tools to scan for vulnerabilities, and enforcing review processes for any new dependencies being added to the codebase.

By integrating these practices into the development process, organisations can build more resilient software, reduce vulnerabilities, and prevent incidents involving malicious dependencies.

Conclusion

As the developer community continues to embrace rapid innovation, understanding the security risks inherent in third-party dependencies is crucial. Adopting preventive measures and enforcing better dependency management practices are vital to mitigate the risks of malicious packages compromising projects, data, and systems. By recognising these threats, developers and startups can secure their software supply chains and build more resilient products.

References & Further Reading

Why Startups Should Put Security First: Push from Five Eyes

Why Startups Should Put Security First: Push from Five Eyes

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Five Eyes intelligence chiefs warn of ‘sharp rise’ in commercial espionage

The Five Eyes nations—Australia, Canada, New Zealand, the UK, and the US—have launched a joint initiative, Secure Innovation, to encourage tech startups to adopt robust security practices. This collaborative effort aims to address the increasing cyber threats faced by emerging technology companies, particularly from sophisticated nation-state actors.

The Growing Threat Landscape

The rapid pace of technological innovation has made startups a prime target for cyberattacks. These attacks can range from intellectual property theft and data breaches to disruption of critical services. A recent report by the Five Eyes alliance highlights that emerging tech ecosystems are facing unprecedented threats. To mitigate these risks, the Five Eyes have outlined five key principles for startups to follow, as detailed in guidance from the National Cyber Security Centre (NCSC):

  1. Know the Threats: Startups must develop a strong understanding of the threat landscape, including potential vulnerabilities and emerging threats. This involves staying informed about the latest cyber threats, conducting regular risk assessments, and implementing effective threat intelligence practices.
  2. Secure the Business Environment: Establishing a strong security culture within the organization is essential. This includes appointing a dedicated security leader, implementing robust access controls, and conducting regular security awareness training for employees. Additionally, startups should prioritize incident response planning and testing to minimize the impact of potential cyberattacks.
  3. Secure Products by Design: Security should be integrated into the development process from the outset. This involves following secure coding practices, conducting regular security testing, and using secure software development frameworks. By prioritizing security from the beginning, startups can reduce the risk of vulnerabilities and data breaches.
  4. Secure Partnerships: When collaborating with third-party vendors and partners, startups must conduct thorough due diligence to assess their security practices. Sharing sensitive information with untrusted partners can expose the startup to significant risks, making it crucial to ensure all partners adhere to robust security standards.
  5. Secure Growth: As startups scale, they must continue to prioritize security. This involves expanding security teams, implementing advanced security technologies, and maintaining a strong security culture. Startups should also consider conducting regular security audits and penetration testing to identify and address potential vulnerabilities.

Why Is Secure by Design So Difficult for Startups?

While the concept of “Secure by Design” is critical, many startups find it challenging to implement due to several reasons:

  1. Limited Resources: Startups often operate on tight budgets, focusing on minimum viable products (MVPs) to prove market fit. Allocating funds to security can feel like a competing priority, especially when the immediate goal is rapid growth.
  2. Time Pressure: The urgency to get products to market quickly means that startups may overlook secure development practices, viewing them as “nice-to-haves” rather than essential components. This rush often leads to security gaps that may only become apparent later.
  3. Talent Shortage: Finding experienced security professionals is difficult, especially for startups with limited financial leverage. Skilled engineers who can integrate security into the development lifecycle are often more interested in established firms that can offer competitive salaries.
  4. Perceived Incompatibility with Innovation: Security measures are sometimes seen as inhibitors to creativity and innovation. Secure coding practices, frequent testing, and code reviews are viewed as processes that slow down development, making startups hesitant to incorporate them during their early stages.
  5. Complexity of Security Requirements: Startups often struggle to understand and implement comprehensive security measures without prior experience or guidance. Security requirements can be perceived as overwhelming, especially for small teams already juggling development, marketing, and scaling responsibilities.

This perceived incompatibility of security with growth, coupled with resource and talent constraints, results in many startups postponing a “secure by design” approach, potentially exposing them to higher risks down the line.

How Startups Can Achieve Secure by Design Architectures

Despite these challenges, achieving a Secure by Design architecture is both feasible and advantageous for startups. Here are key strategies to help build secure foundations:

  1. Hiring and Building a Security-Conscious Team:
    • Early Inclusion of Security Expertise: Hiring a security professional or appointing a security-focused technical co-founder can lay the groundwork for embedding security into the company’s DNA.
    • Upskilling Existing Teams: Startups may not be able to hire dedicated security engineers immediately, but they can train existing developers. Investing in security certifications like CISSP, CEH, or courses on secure coding will improve the team’s overall competency.
  2. Integrating Security into Design and Development:
    • Threat Modeling and Risk Assessment: Incorporate threat modeling sessions early in product development to identify potential risks. By understanding threats during the design phase, startups can adapt their architectures to minimize vulnerabilities.
    • Secure Development Lifecycle: Implement a secure software development lifecycle (SDLC) with consistent code reviews and static analysis tools to catch vulnerabilities during development. Automating security checks using tools like Snyk or OWASP ZAP can help catch issues without slowing development significantly.
  3. Focusing on Scalable Security Frameworks:
    • Microservices Architecture: Startups can consider using a microservices-based architecture. This allows them to isolate services, meaning that a compromise in one area of the product doesn’t necessarily lead to full-system exposure.
    • Zero Trust Principles: Startups should build products with Zero Trust principles, ensuring that every interaction—whether internal or external—is authenticated and validated. Even at an early stage, implementing identity management protocols and ensuring encrypted data flow will create a secure-by-default product.
  4. Investing in Security Tools and Automation:
    • Continuous Integration and Delivery (CI/CD) Pipeline Security: Integrating security checks into CI/CD processes ensures that every code commit is tested for vulnerabilities. Open-source tools like Jenkins can be configured with security plugins, making security an automated and natural part of the development workflow.
    • Use of DevSecOps: Adopting a DevSecOps culture can streamline security implementation. This ensures security practices evolve alongside development processes, rather than being bolted on afterward. DevSecOps also fosters collaboration between development, operations, and security teams.
  5. Leveraging External Support and Partnerships:
    • Partnering with Managed Security Providers: Startups lacking the capacity for in-house security can benefit from partnerships with managed security providers. This allows them to outsource their security needs to experts while they focus on core product development.
    • Utilize Government and Industry Resources: Programs like Secure Innovation and government grants provide startups with the frameworks and sometimes the financial resources needed to adopt security measures without excessive cost burdens.

Conclusion

The Five Eyes’ Secure Innovation initiative is a significant step forward in protecting the interests of tech startups. By embracing these principles and striving for a secure-by-design architecture, startups can not only mitigate cyber risks but also gain a competitive advantage in the marketplace. The key to startup success is integrating security into the heart of product development from the outset, recognizing it as a value-add rather than an impediment.

With the right strategies—whether through hiring, training, automation, or partnerships—startups can create secure and scalable products, build customer trust, and position themselves for long-term success in a competitive digital landscape.

References and Further Reading:

  1. Five Eyes launch Secure Innovation to protect tech sector – Open Access Government
  2. Five Eyes launch shared advice for tech startups – National Cyber Security Centre
  3. Five Eye collaboration at DoDIIS Worldwide – Clearance Jobs
  4. Five Eyes Alliance Unveils Secure Innovation Guidance – ExecutiveGov
Scattered Spider Attacks: Tips for SaaS Security

Scattered Spider Attacks: Tips for SaaS Security

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As cloud adoption soars, threat groups like LUCR-3 Scattered Spider and Oktapus are mastering new ways to exploit identity management systems(IAMs), making these attacks more frequent and harder to detect. By targeting cloud environments and leveraging human vulnerabilities, LUCR-3 compromises identity providers (IDPs) and uses sophisticated techniques to breach organizations.

Before we begin, I wanted to present a random sampling of the successful attacks carried over by the LUCR-3 aka Scattered Spider.

Company/ProductDate AttackedCompromised SystemProjected LossMitigation Time
Telecom Company (Unnamed)December 2022Mobile Carrier Network, IDP SystemsEstimated millions in damagesSeveral weeks (ongoing)​CrowdStrike
Octa (Roasted Oktapus)March 2022Identity Provider (Okta) and SaaSPotential damage to ~366 companies4-5 weeks​Hero​Wikipedia
British TelecommunicationsJune 2022Mobile Carrier Systems, BPO NetworksMillions in lost revenue3-4 weeks​CrowdStrike​Hero
Gaming Company (Unnamed)September 2022Cloud Infrastructure (SaaS and IaaS)Losses in IP theft (unconfirmed)~2 weeks​ISPM ITDR
Cloud Hosting ProviderNovember 2022AWS, Azure Environments, IAM SystemsIP theft and reputational damage3 weeks​CrowdStrike
MGM ResortsSeptember 2023Corporate systems, Help Desk, and IDPMillions in lost revenueSystems offline for weeks​Wikipedia
Caesars EntertainmentSeptember 2023Identity Providers (IDP) and SaaS~$30 million ransom paid​ Wikipedia~1 month recovery​Cyber Defense Magazine
Charter CommunicationsApril 2024Cloud-based systems (Okta phishing)Potentially millions in damages​ ResilienceSeveral weeks
NHS Hospitals (UK)June 2024VMware ESXi servers, critical healthcare systemsDisruption of hundreds of operations​BleepingComputerOngoing​BleepingComputer
Synnovis Pathology ServicesJune 2024Ransomware on pathology services systemsEstimated millions in healthcare disruptions​BleepingComputerOngoing investigation​BleepingComputer
This table provides a detailed overview of Scattered Spider’s recent attacks across industries, demonstrating their evolving tactics and widespread impact.

This article outlines the technical steps LUCR-3 typically follows, from initial access to persistence and lateral movement within cloud environments, mostly targeting SaaS platforms.

Step 1: Initial Access Through Identity Compromise

LUCR-3 starts with a core weakness in modern security—identity management. Their main attack vectors include:

  1. SIM Swapping: LUCR-3 hijacks a user’s phone number by tricking the telecom provider into assigning the number to a new SIM card. Once they have control over the phone number, they can intercept One-Time Passwords (OTP) sent via SMS.
  2. MFA Fatigue: The attackers flood the target with repeated MFA prompts, often overwhelming them into approving a malicious login request.
  3. Phishing and Social Engineering: They set up fake login pages for SaaS applications (e.g., SharePoint or OneDrive), capturing legitimate credentials and OTP codes.

These techniques allow LUCR-3 to bypass standard Multi-Factor Authentication (MFA) protections and gain access to cloud environments​

ISPM ITDR, Hero.

Step 2: Bypassing MFA and Establishing a Foothold

Once inside, LUCR-3 focuses on maintaining access to the compromised identity. This is done by modifying the victim’s MFA settings. Their tactics include:

  • Registering New Devices: LUCR-3 will register their own devices (phones or emails) under the victim’s account, which ensures they can log in without triggering alerts. For example, they might register an iPhone if the victim previously used Android, raising minimal suspicion.
  • Adding Alternate MFA Methods: They add backup MFA methods, such as an external email address, making it even harder to lock them out if the breach is discovered​ISPM ITDR.

Step 3: Reconnaissance and Data Collection in SaaS Environments

After gaining access to cloud platforms, LUCR-3 conducts extensive reconnaissance to identify critical assets, credentials, and sensitive information. Here’s how they do it:

  1. SaaS Platforms: They use native tools within platforms like SharePoint, OneDrive, and Salesforce to search for documents containing passwords, intellectual property, or financial data. They operate like legitimate users to avoid detection.
  2. AWS Cloud: In AWS environments, LUCR-3 navigates the AWS Management Console, targeting services like EC2 (Elastic Compute Cloud) and S3 (Simple Storage Service). They leverage the AWS-GatherSoftwareInventory job through Systems Manager (SSM) to list running software across EC2 instances​ ISPM ITDR.
  3. Privilege Escalation: LUCR-3 may modify IAM roles or escalate privileges by updating LoginProfiles or creating new access keys, ensuring they have continued administrative access​Hero.

Step 4: Lateral Movement and Persistence

LUCR-3 ensures they have multiple ways to re-enter a compromised environment, even if one of their entry points is discovered. Here’s how they achieve persistence:

  1. Create New IAM Users: LUCR-3 creates new user accounts that align with the naming conventions of the compromised environment to avoid suspicion. These accounts often have high-level access, allowing them to continue accessing the environment even after the initial breach is patched.
  2. Secrets Harvesting: Using tools like S3 Browser, LUCR-3 harvests credentials stored in AWS Secrets Manager and similar services, allowing them to steal sensitive data and further penetrate systems​ Hero.
  3. MFA Manipulation: They alter MFA settings to ensure continued access, often registering additional email addresses or devices that align with the compromised identity.

Step 5: Data Exfiltration and Extortion

Once LUCR-3 has gained the necessary access and gathered sensitive data, they execute their final stage of the attack, which often involves extortion. The data collected during their reconnaissance, such as customer information or proprietary code, is used as leverage to demand payment from the compromised organization​ The Hacker News​ ISPM ITDR.

How to Detect and Prevent LUCR-3 Attacks

Given LUCR-3’s sophisticated techniques, organizations must adopt advanced security measures to detect and mitigate such attacks:

  • Monitor MFA Changes: Keep a close watch for unusual changes in MFA settings, such as new device registrations or changes from app-based authentication to SMS-based methods.
  • Audit Cloud Logs: Regularly audit cloud environments, especially IAM policy changes, new access key creation, and suspicious activity in management consoles.
  • Behavioral Anomaly Detection: Implement advanced behavioral monitoring to detect when legitimate accounts are being used in unusual ways, such as accessing unfamiliar services or using unfamiliar devices.

Conclusion

LUCR-3 (Scattered Spider) represents a new breed of cyber threat actors that rely on identity compromise rather than malware or brute force. By targeting the very foundation of security—identity—they can infiltrate cloud environments, move laterally, and exfiltrate data with relative ease. As organizations increasingly rely on cloud services, strengthening identity management, closely monitoring for anomalies, and responding quickly to suspicious behavior are critical defenses against such attacks.

References and Further Reading

  1. The Hacker News: Provides a detailed breakdown of LUCR-3’s identity-based attacks across cloud environments, lateral movement techniques, and persistence strategies.
  2. Permiso.io: Discusses how LUCR-3 targets identity infrastructure, modifies MFA settings, and maintains persistence in cloud environments like AWS and Azure.
  3. CrowdStrike: Offers insights into Scattered Spider’s use of the Bring-Your-Own-Vulnerable-Driver (BYOVD) technique and their focus on telecom and BPO sectors.
  4. Resilience Cyber Research: Highlights recent phishing campaigns by LUCR-3 in 2024, targeting industries such as telecom, food services, and tech, using Okta-based phishing tactics.
  5. EclecticIQ: Discusses LUCR-3’s involvement in ransomware attacks targeting cloud infrastructures within the insurance and financial sectors, leveraging smishing and phishing techniques.
  6. Wikipedia (Scattered Spider): Overview of the MGM Resorts hack in 2023, detailing how Scattered Spider gained access to internal systems through social engineering and caused significant disruptions.
  7. Cyber Defense Magazine: Discusses how LUCR-3 has highlighted vulnerabilities in MFA and cloud security, predicting more targeted attacks on SaaS and cloud service providers.
  8. BleepingComputer: Provides an overview of LUCR-3’s collaboration with ransomware groups like Qilin, targeting high-profile companies such as MGM Resorts and healthcare services.
  9. Caesars and MGM Hacking Incident: Outlines how Caesars Entertainment suffered a breach in September 2023, paying a ~$30 million ransom, while MGM Resorts experienced extensive downtime following a similar attack.
  10. Microsoft and Qilin Ransomware: Microsoft linked Scattered Spider to ransomware attacks using the Qilin variant, affecting companies like Synnovis Pathology and NHS hospitals in 2024. Read more​ BleepingComputer​ Wikipedia

These resources offer in-depth insights into the attack strategies and defence mechanisms relevant to LUCR-3 (Scattered Spider), perfect for anyone looking to deepen their understanding of identity-based attacks and cloud security.

How Will China’s Quantum Advances Change Internet Security?

How Will China’s Quantum Advances Change Internet Security?

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Image Generated with Dalle 3

Introduction:

Chinese scientists have recently announced that they have successfully cracked military-grade encryption using a quantum computer with 372 qubits, a significant achievement that underscores the rapid evolution of quantum technology. This breakthrough has sparked concerns across global cybersecurity communities as RSA-2048 encryption—a widely regarded standard—was reportedly compromised. However, while this development signifies an important leap forward in quantum capabilities, its immediate implications are nuanced, particularly for everyday encryption protocols.

Drawing on technical insights from recent papers and analyses, this article delves deeper into the technological aspects of the breakthrough and explores why, despite this milestone, quantum computing still has limitations that prevent it from immediately threatening personal and business-level encryption.

The Quantum Breakthrough: Factoring RSA-2048

As reported by The Quantum Insider and South China Morning Post, the Chinese research team employed a 372-qubit quantum computer to crack RSA-2048 encryption, a cryptographic standard widely used to protect sensitive military information. RSA encryption relies on the difficulty of factoring large numbers, a task that classical computers would take thousands of years to solve. However, using quantum algorithms—specifically an enhanced version of Shor’s algorithm—the team demonstrated that quantum computers could break RSA-2048 in a much shorter time frame.

The breakthrough optimised Shor’s algorithm to function efficiently within the constraints of a 372-qubit machine. This marks a critical turning point in quantum computing, as it demonstrates the potential for quantum systems to tackle problems previously considered infeasible for classical systems. However, the paper from the Chinese Journal of Computers (2024) offers deeper insights into the quantum architecture and algorithmic refinements that made this breakthrough possible, highlighting both the computational power and limitations of the system.

Quantum Hardware and Algorithmic Optimisation

The technical aspects of the Chinese breakthrough, as detailed in the 2024 paper published in the Chinese Journal of Computers (CJC), emphasise the improvements in quantum hardware and algorithmic approaches that were key to this success. The paper outlines how the researchers enhanced Shor’s algorithm to mitigate the high error rates commonly associated with quantum computing, allowing for more stable computations over longer periods. This required optimising quantum gate operations, reducing quantum noise, and employing error-correction codes to preserve the integrity of qubit states.

Despite these improvements, the paper makes it clear that current quantum computers, including the 372-qubit machine used in this experiment, still suffer from several limitations. The system required an extremely controlled environment to maintain qubit coherence, and any deviation from ideal conditions would have introduced significant errors. Furthermore, the researchers faced challenges related to the scalability of the system, as error rates increase exponentially with the number of qubits involved. These limitations are consistent with the broader consensus in the field, as noted by Bill Buchanan and other experts, that practical quantum decryption on a global scale is not yet feasible.

The CJC paper also points out that while the breakthrough is impressive, it does not represent a complete realisation of quantum supremacy—the point at which quantum computers outperform classical computers across a wide range of tasks. The paper discusses the need for further advancements in quantum gate fidelity, qubit interconnectivity, and error correction to make quantum decryption scalable and applicable to broader, real-world encryption protocols.

Technical Analysis based on Li et al. (2024):

The paper explores two approaches for attacking RSA public key cryptography using quantum annealing:

1. Quantum Annealing for Combinatorial Optimization:

  • Method: This approach translates the mathematical attack method into a combinatorial optimization problem suited for the Ising model or QUBO model [1]. The Ising model represents a system of interacting spins, which can be mapped to the problem of factoring large integers used in RSA encryption.
  • Key Contribution: The paper proposes a high-level optimization model for multiplication tables and establishes a new dimensionality reduction formula. This formula reduces the number of qubits needed, thus saving resources and improving the stability of the Ising model [1]. The authors demonstrate this by successfully decomposing a two-million-level integer using a D-Wave Advantage system.
  • Comparison: This approach outperforms previous methods by universities and corporations like Purdue, Lockheed Martin, and Fujitsu [1]. This is achieved by significantly reducing the range of coefficients required in the Ising model, leading to a higher success rate in decomposition.
  • Focus: This technique represents a class of attack algorithms specifically designed for D-Wave quantum computers, known for their use of quantum annealing [1].

2. Quantum Annealing with Classical Methods:

  • Method: This approach combines the quantum annealing algorithm with established mathematical methods for cryptographic attacks, aiming to optimize attacks on specific cryptographic components [1]. It integrates the classical lattice reduction algorithm with the Schnorr algorithm.
  • Key Contribution: The authors leverage the quantum tunneling effect to adjust the rounding direction within the Babai algorithm, allowing for precise vector determination, a crucial step in the attack [1]. Quantum computing’s exponential acceleration capabilities address the challenge of calculating numerous rounded directions, essential for solving lattice problems [1]. Additionally, the paper proposes methods to improve search efficiency for close vectors, considering both qubit resources and time costs [1]. Notably, it demonstrates the first 50-bit integer decomposition on a D-Wave Advantage system, showcasing the algorithm’s versatility [1].
  • Comparison: The paper argues that D-Wave quantum annealing offers a more practical approach for smaller-scale attacks compared to Variational Quantum Algorithms (VQAs) on NISQ (Noisy Intermediate-Scale Quantum) computers. VQAs suffer from the “barren plateaus” problem, which can hinder algorithm convergence and limit effectiveness [1]. Quantum annealing is less susceptible to this limitation and offers an advantage when dealing with smaller-scale attacks.

Citations:

  1. Li, Gao, et al. “A Novel Quantum Annealing Attack on RSA Public Key Cryptosystems.” WC 2024 (2024).

Implications for Civilian Encryption: Limited Immediate Impact

While the Chinese breakthrough is undeniably significant, it is essential to recognise that the decryption of military-grade encryption does not immediately translate to vulnerabilities in civilian encryption protocols. Most personal and business communications rely on RSA-1024, elliptic-curve cryptography (ECC), or other lower-bit encryption systems. These systems remain secure against the capabilities of today’s quantum computers.

Moreover, as highlighted in the paper by Buchanan and echoed in the CJC analysis, many organisations are already transitioning towards post-quantum cryptography (PQC). PQC algorithms are specifically designed to withstand quantum attacks, ensuring that even as quantum computers advance, encryption systems will evolve to meet new threats.

Another key point raised by the CJC paper is that quantum decryption requires an immense amount of resources and computational power. The system used to break RSA-2048 involved highly specialised hardware and extensive computational time. Scaling such an operation to break everyday encryption protocols, such as those used in internet banking or personal communications, would require quantum computers with far more qubits and error-correction capabilities than are currently available.

Preparing for a Quantum Future: Post-Quantum Cryptography

As quantum computing technology evolves, it is imperative that governments, companies, and cybersecurity professionals continue preparing for the eventual reality of quantum decryption. This preparation includes developing and implementing post-quantum cryptographic solutions that are immune to quantum attacks. The National Institute of Standards and Technology (NIST) has already initiated efforts to standardise post-quantum cryptographic algorithms, which are designed to be secure against both classical and quantum attacks. The CJC paper underlines the importance of this transition and suggests that PQC will likely become the new standard in encryption over the next decade.

In addition to PQC, the CJC paper highlights the need for ongoing research into hybrid encryption systems, which combine classical cryptographic techniques with quantum-resistant methods. These hybrid systems could provide a transitional solution, allowing existing infrastructure to remain secure while fully quantum-resistant algorithms are developed and implemented.

Conclusion: A Scientific Milestone with Limited Immediate Consequences

The Chinese research team’s quantum decryption of military-grade encryption is a groundbreaking scientific achievement, signalling that quantum computing is rapidly advancing towards practical applications. However, as emphasised in the technical analyses from the Chinese Journal of Computers and other sources, this breakthrough is not yet a direct threat to civilian encryption systems. Current quantum computers remain limited by their error rates, scalability challenges, and the need for controlled environments, preventing widespread decryption capabilities.

As organisations and governments prepare for a post-quantum future, the adoption of post-quantum cryptography and hybrid systems will be crucial in ensuring that encryption protocols remain robust against both classical and quantum threats. While the breakthrough highlights the potential power of quantum computing, its impact on everyday encryption is still years, if not decades, away.

References and Further Reading

  1. Bill Buchanan, “A Major Advancement on Quantum Cracking,” Medium, 2024.
  2. The Quantum Insider, “Chinese Scientists Report Using Quantum Computer to Hack Military-Grade Encryption,” October 11, 2024.
  3. South China Morning Post, “Chinese Scientists Hack Military-Grade Encryption Using Quantum Computer,” October 2024.
  4. Interesting Engineering, “China’s Scientists Successfully Hack Military-Grade Encryption with Quantum Computer,” October 2024.
  5. Shor, P.W., “Algorithms for Quantum Computation: Discrete Logarithms and Factoring,” Proceedings of the 35th Annual Symposium on Foundations of Computer Science, 1994.
  6. National Institute of Standards and Technology (NIST), “Post-Quantum Cryptography: Current Status,” 2024.
  7. Chinese Journal of Computers, “Quantum Algorithmic Enhancements in Breaking RSA-2048 Encryption,” 2024.
Starling Bank’s Penalty: How to Strengthen Your Compliance Efforts

Starling Bank’s Penalty: How to Strengthen Your Compliance Efforts

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Introduction

The rapid growth of the fintech industry has brought with it immense opportunities for innovation, but also significant risks in terms of regulatory compliance and real security. Starling Bank, one of the UK’s prominent digital banks, recently faced a £29 million fine in October 2024 from the Financial Conduct Authority (FCA) for serious lapses in its anti-money laundering (AML) and sanctions screening processes. This fine is part of a broader trend of fintechs grappling with regulatory pressures as they scale quickly. Failures in compliance not only lead to financial penalties but also damage to reputation and customer trust. In most cases, it also leads to revenue loss and or a significant business impact.

In this article, we explore what went wrong at Starling Bank, examine similar compliance issues faced by other major financial institutions like Paytm, Monzo, HDFC, Axis Bank & RobinHood and propose practical solutions to help fintech companies strengthen their compliance frameworks. This also helps to establish the point that these cybersecurity and compliance control lapses are not restricted to geography and are prevalent in the US, UK, India and many other regions. Additionally, we dive into how vulnerabilities manifest in growing fintechs and the increasing importance of adopting zero-trust architectures and AI-powered AML systems to safeguard against financial crime.

Background

In October 2024, Starling Bank was fined £29 million by the Financial Conduct Authority (FCA) for significant lapses in its anti-money laundering (AML) controls and sanctions screening. The penalty highlights the increasing pressure on fintech firms to build robust compliance frameworks that evolve with their rapid growth. Starling’s case, although high-profile, is just one in a series of incidents where compliance failures have attracted regulatory action. This article will explore what went wrong at Starling, examine similar compliance failures across the global fintech landscape, and provide recommendations on how fintechs can enhance their security and compliance controls.

What Went Wrong and How the Vulnerability Manifested

The FCA investigation into Starling Bank uncovered two major compliance gaps between 2019 and 2023, which exposed the bank to financial crime risks:

  1. Failure to Onboard and Monitor High-Risk Clients: Starling’s systems for onboarding new clients, particularly high-risk individuals, were not sufficiently rigorous. The bank’s AML mechanisms did not scale in line with the rapid increase in customers, leaving gaps where sanctioned or suspicious individuals could go undetected. Despite the bank’s growth, the compliance framework remained stagnant, resulting in breaches of Principle 3 of the FCA’s regulations for businesses​(Crowdfund Insider)​(FinTech Futures).
  2. Inadequate Sanctions Screening: Starling’s sanctions screening systems failed to adequately identify transactions from sanctioned entities, a critical vulnerability that persisted for several years. With insufficient real-time monitoring capabilities, the bank did not screen many transactions against the latest sanctions lists, leaving it exposed to potentially illegal activity​(FinTech Futures). This is especially concerning in a financial ecosystem where transactions are frequent and high in volume, requiring robust systems to ensure compliance at all times.

These vulnerabilities manifested in Starling’s inability to effectively prevent financial crime, culminating in the FCA’s action in October 2024.

Learning from Similar Failures in the Fintech Industry

  1. Paytm’s Cybersecurity Breach Reporting Delays (October 2024): In India, Paytm was fined for failing to report cybersecurity breaches in a timely manner to the Reserve Bank of India (RBI). This non-compliance exposed vulnerabilities in Paytm’s internal governance structures, particularly in their failure to adapt to rapid business expansion and manage cybersecurity threats​(Reuters).
  2. HDFC and Axis Banks’ Regulatory Breaches (September 2024): The RBI fined HDFC Bank and Axis Bank in September 2024 for failing to comply with regulatory guidelines, emphasizing how traditional banks, like fintechs, can face compliance challenges as they scale. The fines were related to lapses in governance and risk management frameworks​(Economic Times).
  3. Monzo’s PIN Security Breach (2023): In 2023, UK-based challenger bank Monzo experienced a breach where customer PINs were accidentally exposed due to an internal vulnerability. Although Monzo responded swiftly to mitigate the damage, the breach illustrated the need for fintechs to prioritize backend security and implement zero-trust security architectures that can prevent such incidents​(Wired).
  4. LockBit Ransomware Attack (2024): The LockBit ransomware attack on a major financial institution in 2024 demonstrated the growing cyber threats that fintechs face. This attack exposed the weaknesses in traditional cybersecurity models, underscoring the necessity of adopting zero-trust architectures for fintech companies to protect sensitive data and transactions from malicious actors​(NCSC).
  5. Robinhood’s Regulatory Scrutiny (2021-2022): In June 2021, Robinhood was fined $70 million by FINRA for misleading customers, causing harm through platform outages, and failing to manage operational risks during the GameStop trading frenzy. Robinhood’s systems were not equipped to handle the surge in trading volumes, leading to severe service disruptions and a failure to communicate risks to customers.
  6. Robinhood Crypto’s Cybersecurity Failure (2022): In August 2003, Robinhood was fined $30 million by the New York State Department of Financial Services (NYDFS) for failing to comply with anti-money laundering (AML) regulations and cybersecurity obligations related to its cryptocurrency trading operations. The fine was issued due to inadequate staffing, compliance failures, and improper handling of regulatory oversight within its crypto business. Much like Starling, Robinhood’s compliance systems lagged behind its rapid business growth​ (Compliance Week)

Key Statistics in the Fintech Compliance Landscape

  • 65% of organizations in the financial sector had more than 500 sensitive files open to every employee in 2023, making them highly vulnerable to insider threats​.
  • The average cost of a data breach in financial services was $5.85 million in 2023, a significant figure that shows the financial impact of security vulnerabilities​.
  • 27% of ransomware attacks targeted financial institutions in 2022, with the number of attacks continuing to rise in 2024, further highlighting the importance of robust cybersecurity frameworks​.
  • 81% of financial institutions reported a rise in phishing and social engineering attacks in 2023, emphasizing the need for employee awareness and strong access controls​.
  • By 2025, the global cost of cybercrime is projected to exceed $10.5 trillion annually, a figure that will disproportionately impact fintech companies that fail to implement strong security protocols​.

Recommendations for Strengthening Compliance and Security Controls

To prevent future compliance breaches, fintech firms should prioritise scalable, technology-enabled compliance solutions. This requires empowering Compliance Heads, Information Security Teams, CISOs, and CTOs with the necessary budgets and authority to develop secure-by-design environments, teams, infrastructure, and products.

  1. AI-Powered AML Systems: Leverage artificial intelligence (AI) and machine learning to enhance AML systems. These technologies can dynamically adjust to new threats and process high volumes of transactions to detect suspicious patterns in real time. This approach will ensure that fintechs can comply with evolving regulatory requirements while scaling.
  2. Zero-Trust Security Models: As the LockBit ransomware attack showed in 2024, fintechs must adopt zero-trust architectures, where every user and device interacting with the system is continuously authenticated and verified. This reduces the risk of internal breaches and external attacks​(Cloudflare).
  3. Real-Time Auditing and Blockchain for Transparency: Real-time auditing, combined with blockchain technology, provides an immutable and transparent record of all financial transactions. This would help fintechs like Starling avoid the pitfalls of delayed sanctions screening, as blockchain ensures immediate and traceable compliance checks​(EY).
  4. Multi-Layered Sanctions Screening: Implement a multi-layered sanctions screening system that combines automated transaction monitoring with manual oversight for high-risk accounts. This dual approach ensures that fintechs can monitor suspicious activities while maintaining compliance with global regulatory frameworks​(Exiger)​(FinTech Futures).
  5. Continuous Employee Training and Governance: Strong governance structures and regular compliance training for employees will ensure that fintechs remain agile and responsive to regulatory changes. This prepares the organization to adapt as new regulations emerge and customer bases expand.

Conclusion

The £29 million fine imposed on Starling Bank in October 2024 serves as a crucial reminder for fintech companies to integrate robust compliance and security frameworks as they grow. In an industry where regulatory scrutiny is intensifying, the fintech players that prioritize compliance will not only avoid costly fines but also position themselves as trusted institutions in the financial services world.

Further Reading and References

  1. RBI Fines HDFC, Axis Bank for Non-Compliance with Regulations (September 2024)
  2. RBI Fines Paytm for Not Reporting Cybersecurity Breaches on Time (October 2024)
  3. LockBit’s Latest Attack Shows Why Fintech Needs More Zero Trust (2024)
  4. Monzo PIN Security Breach Explained (2023)
  5. Varonis Cybersecurity Statistics (2023)

Scholarly Papers & References

  1. Barr, M.S.; Jackson, H.E.; Tahyar, M. Financial Regulation: Law and Policy. SSRN Scholarly Paper No. 3576506, 2020. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3576506
  2. Suryono, R.R.; Budi, I.; Purwandari, B. Challenges and Trends of Financial Technology (Fintech): A Systematic Literature Review. Information 2020, 11, 590. https://doi.org/10.3390/info11120590
  3. AlBenJasim, S., Dargahi, T., Takruri, H., & Al-Zaidi, R. (2023). FinTech Cybersecurity Challenges and Regulations: Bahrain Case Study. Journal of Computer Information Systems, 1–17. https://doi.org/10.1080/08874417.2023.2251455

By learning from past failures and adopting stronger controls, fintechs can mitigate the risks of financial crime, protect customer data, and ensure compliance in an increasingly regulated industry.

Why Did Elastic Decide to Go Open Source Again?

Why Did Elastic Decide to Go Open Source Again?

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Elastic’s Return to Open Source: The Knight is back to the Pavilion

Elastic, the company behind Elasticsearch, recently decided to revert to an open-source licensing model after four years of operating under a proprietary license. This decision reflects a shift in strategy that emphasizes community-driven innovation and collaboration. In 2019, Elastic initially adopted a proprietary model to protect its intellectual property from cloud providers like Amazon Web Services (AWS), which were benefiting from Elasticsearch without contributing to its development. However, the move away from open-source posed its own challenges, including alienating the developer community that had helped build Elasticsearch into a widely-used tool.

In 2024, Elastic CEO Shay Banon announced the company’s return to an open-source framework. He explained that this decision stems from the belief that open collaboration fosters innovation and better serves the long-term interests of both the company and its user base. “We believe that the best products are built together,” Banon stated, emphasizing the value of community engagement in product development.

Recent Changes in Open-Source Licensing Models

Elastic’s decision is not an isolated incident. Over the past few years, several other technology companies have reconsidered their licensing models in response to the changing dynamics of software development and cloud service providers. These companies have struggled with how to balance open-source principles with the need to protect their commercial interests.

  1. Redis Labs
    Redis Labs initially licensed Redis under a permissive open-source license, but in 2018, the company adopted the Commons Clause to prevent cloud providers from offering Redis as a service without contributing to its development. However, after facing backlash from the developer community, Redis Labs adjusted its approach by introducing Redis Stack under more community-friendly terms, highlighting the difficulty of maintaining open-source integrity while ensuring business protection.
  2. HashiCorp
    In 2023, HashiCorp, known for popular tools like Terraform, adopted a Business Source License (BSL), which restricts the usage of its software in certain commercial contexts. HashiCorp’s move was driven by concerns over cloud providers monetizing its tools without contributing back to the open-source community. While BSL is not a traditional open-source license, HashiCorp continues to maintain a balance between openness and protecting its intellectual property, showing how companies are navigating complex market dynamics.
  3. MongoDB
    MongoDB’s shift to the Server Side Public License (SSPL) in 2018 was another major development in the open-source licensing debate. The SSPL aims to prevent cloud service providers from exploiting MongoDB’s open-source code without contributing back. While the SSPL is more restrictive than traditional open-source licenses, MongoDB’s goal was to retain the open-source ethos while ensuring that cloud vendors could not commercialize the software without contributing to its development.
  4. Chef Software
    Chef, an automation tool provider, switched all of its products to open-source in 2019 after years of operating under a mixed licensing model. This shift was largely a response to the growing demand for transparency and community collaboration. Chef’s decision allowed it to rebuild trust within its user base and align its business strategy with the broader trends in software development.

Impact on the Average Software Developer

For the average software developer, these licensing model changes can profoundly impact their work, career growth, and day-to-day development practices.

  1. Access to Cutting-Edge Tools
    When companies like Elastic and MongoDB return to open-source models, developers gain unrestricted access to powerful tools and frameworks. This democratizes the technology, allowing developers from small companies, startups, and even personal projects to leverage the same tools that major enterprises use, without the barrier of expensive proprietary licenses. For many developers, open-source provides not just tools, but an entire ecosystem for experimentation, learning, and rapid prototyping.
  2. Contributing to Open-Source Communities
    Open-source contributions are an essential career-building tool for many developers. By contributing to open-source projects, developers can gain real-world experience, build portfolios, and even influence the direction of widely-used technologies. When companies like HashiCorp and Redis Labs shift their focus back to open-source, it increases opportunities for developers to become part of a larger, global development community.
  3. Career and Learning Opportunities
    Exposure to open-source projects allows developers to work with cutting-edge technology and methodologies. This can accelerate learning, as open-source projects are often evolving quickly with input from diverse and global teams. Additionally, contributing to popular open-source projects like Elastic or Kubernetes can greatly enhance a developer’s resume and open doors to career opportunities, including job offers and consulting roles.
  4. Navigating Licensing Restrictions
    Developers must also become more adept at navigating the complexities of new licenses like SSPL and BSL. These licenses place restrictions on how open-source software can be used, especially in cloud environments. Understanding the fine print is crucial for developers working in enterprise environments or launching their own SaaS products, as improper use of open-source software can lead to legal complications. This makes legal and compliance knowledge increasingly important in modern software development roles.

Open Source vs. Open Governance: A Crucial Distinction

Elastic’s journey highlights a key debate in the software development world: the difference between open source and open governance. While many companies have embraced open-source models, few have transitioned to open governance frameworks, which involve community-driven decision-making for the project’s future direction.

As highlighted in my previous article, “Open Source vs. Open Governance: The State and Future of the Movement,” the distinction lies in control. In open-source projects, the code is freely available, but decisions regarding the project’s roadmap and key developments may still be controlled by a single entity, such as a company. In contrast, open governance ensures that decision-making is decentralized, often involving multiple stakeholders, including developers, users, and companies that contribute to the project.

For Elastic and others, returning to open-source doesn’t necessarily mean embracing open governance. Although Elastic’s code will be open for contributions, the strategic direction will still be managed by the company. This is a common approach in many high-profile open-source projects. For example, Google’s Kubernetes operates under the open-source model but is governed by a diverse group of stakeholders, ensuring the project’s direction isn’t controlled by a single entity. On the other hand, projects like OpenStack follow a more open governance approach, with broader community involvement in decision-making.

Understanding the difference between open-source and open governance is critical as the software industry evolves. Companies are beginning to realize that open-source alone doesn’t always translate into the collaborative, community-driven development they seek. Open governance provides a framework for more inclusive decision-making, but it also presents challenges in terms of efficiency and control.

Looking Ahead: Open Source as a Business Strategy

The return of Elastic and other companies to more open models indicates a growing recognition of the importance of open-source in the software industry. For Elastic, this decision is about more than just licensing; it’s about reconnecting with a developer community that thrives on transparency and collaboration. By embracing open-source again, Elastic hopes to accelerate product development and foster stronger relationships with users.

This broader trend shows that while companies are still cautious about cloud providers exploiting their software, they are increasingly finding ways to leverage open-source models as a business strategy. These recent changes to licensing frameworks highlight the evolving nature of software development and the role open-source plays in it.

For organizations navigating the complex decision between proprietary and open-source models, the key lesson from Elastic’s experience is that the long-term benefits of community-driven development and innovation can outweigh the short-term protection of proprietary models. As more companies follow suit, it’s clear that open-source is not just a technical choice—it’s a business strategy.

Further Reading:

  1. Why Open Source Matters for Innovation – Alan Turing Institute
  2. The Future of Open Source: What to Expect in 2024 and Beyond – MIT Technology Review
  3. Why Every Company Should be Open-Source Aligned – Forbes

References:

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