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The five security principles driving open source security apps at scale

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Editor’s note: Louis will lead an editorial roundtable on this topic at VB Transform this month. Register today. Open-source AI is shaping the future of cybersecurity innovation, consistently breaking down barriers and […]

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more


Editor’s note: Louis will lead an editorial roundtable on this topic at VB Transform this month. Register today.

Open-source AI is shaping the future of cybersecurity innovation, consistently breaking down barriers and delivering results. Its impact spans from agile startups to Cisco‘s Foundation-Sec-8B model, which was downloaded over 18,000 times in just the last month and over 40,000 times since launch.

VentureBeat is seeing the trend accelerating, especially in cybersecurity startups that are bringing a new level of intensity to turning roadmaps into revenue-producing products. Based on months of interviews with startup founders, open-source AI is now indispensable to them and their teams when it comes to fast-tracking concepts to completed, shippable code.

Databricks’ recently announced partnership with Noma Security demonstrates how startups leveraging open-source AI are rapidly disrupting legacy cybersecurity providers by achieving accelerated time-to-market and substantial operational maturity. Cisco’s President and Chief Product Officer Jeetu Patel spoke to the critical shift at RSAC 2025, “AI is fundamentally changing everything, and cybersecurity is at the heart of it all. We’re no longer dealing with human-scale threats; these attacks are occurring at machine scale.”

VentureBeat’s numerous interviews with cybersecurity industry leaders, particularly founders, reveal that open-source AI is essential for enabling businesses to sharpen their focus on key unmet needs across the broad base of enterprise prospects they successfully turn into customers. While open-source AI and the wider software industry drive unprecedented levels of new venture creation and innovation, they also fuel a growing paradox encompassing security, compliance and monetization.

VentureBeat continues to see successful cybersecurity startups navigate these complexities and discover new strengths in their apps, tools, and platforms that weren’t anticipated when they were first created and delivered.

The best-run startups are quick to capitalize on these unforeseen strengths and apply a more disciplined and deliberate approach to governance, recognizing the long-term benefits of that strategy. They’re also faster in adopting as much automation as possible. Most impressive is how they view themselves as building communities for decades to come, all predicated on the ability to pivot product strategy on open source.

Decoding the open source paradox

Open-source AI’s ability to act as an innovation catalyst is proven. What is unknown is the downside or the paradox that’s being created with the all-out focus on performance and the ubiquity of platform development and support. At the center of the paradox for every company building with open-source AI is the need to keep it open to fuel innovation, yet gain control over security vulnerabilities and the complexity of compliance.

Gartner’s Hype Cycle for Open-Source Software, 2024, highlights this stark contradiction, noting that high-risk vulnerabilities within open-source codebases surged 26% annually and now average nearly three years before resolution.

At RSAC 2025, Diana Kelly, CTO of Protect AI, crystallized the stakes during her session titled Principles of GenAI Security: Foundations for Building Security In. She said that “organizations routinely download open-source AI models without adequate security checks, significantly amplifying vulnerability risks.”

Regulatory compliance is becoming more complex and expensive, further fueling the paradox. Startup founders, however, tell VentureBeat that the high costs of compliance can be offset by the data their systems generate.

They’re quick to point out that they do not intend to deliver governance, risk, and compliance (GRC) solutions; however, their apps and platforms are meeting the needs of enterprises in this area, especially across Europe. With enforcement of the EU AI Act imminent, Prompt Security CEO Itamar Golan emphasized the urgency of embedding compliance at the strategic core during an interview completed earlier this year with VentureBeat. “EU AI Act, for example, is starting its enforcement in February, and the pace of enforcement and fines is much higher and aggressive than GDPR. From our perspective, we want to help organizations navigate those frameworks, ensuring they’re aware of the tools available to leverage AI safely and map them to risk levels dictated by the Act.”

Golan further explained, “A very big portion of the current cybersecurity market is derived only from GDPR, and as I see it, the AI regulation is going to be much more aggressive than GDPR. It’s very rational that by around 2028, a very big market will be allocated to AI compliance.”

Nearly every cybersecurity startup founder VentureBeat has interviewed over the last five years mentions how contributing to the open-source community is core to the company they’re creating. Many strive to make this one of the core elements of their business DNA.

The most successful cybersecurity startups realize that making ongoing, significant contributions to open-source communities builds sustainable competitive advantages and industry leadership. Cisco’s Foundation-Sec-8B model exemplifies how targeted, purpose-built cybersecurity tools substantially enhance overall community resilience. The Foundation-Sec-8B model has been downloaded 18,278 times in the last 30 days alone, according to its page on Hugging Face. Foundation Sec-8B is an 8 billion parameter model that can be fine-tuned for specific use cases, including threat detection and auto-remediation.

Meta’s AI Defenders Suite and ProjectDiscovery’s Nuclei further illustrate how focused open-source contributions significantly improve ecosystem security and industry-wide collaboration.

Niv Braun, Co-founder and CEO of Noma Security, reinforced the critical importance of sustained community-building strategies during a recent interview, telling VentureBeat, “The community we’re building is much, much more valuable and will be much more long-lasting than any yearly revenue figure. Building a community that people rely on is absolutely critical”.

Key Takeaways from open-source cybersecurity leaders

Drawing on insights from Braun, Golan, Kelly, Patel, and over a dozen interviews with cybersecurity founders, CEOs, and leaders, five key takeaways emerge as foundational to succeeding with open-source AI. They are as follows:

  1. Embed governance strategically
    Establish an Open Source Program Office (OSPO) to manage licensing, compliance, and vulnerabilities centrally. Embed governance dashboards directly into products, offering real-time regulatory compliance visibility as core differentiation. Braun highlighted governance’s transformative potential during his recent interview with VentureBeat, saying, “Governance isn’t overhead—it’s our key differentiator, enabling seamless compliance.”
  1. Automate security aggressively with generative AI
    Implement generative AI extensively to automate security processes, including vulnerability detection, remediation, and real-time threat management. As Golan articulates clearly: “Generative AI-driven automation dramatically streamlines operations and enhances security efficiency beyond manual capabilities.”
  1. Strategically contribute purpose-built tools
    Actively contribute specialized, purpose-built cybersecurity models back into open-source communities, enhancing collective security resilience. Jeetu Patel succinctly captured this perspective during his keynote at RSAC and interview with VentureBeat: “The true enemy isn’t our competitor. It’s the adversary. Purpose-built open-source contributions are critical for collective cybersecurity resilience.”
  1. Proactively manage and transparently communicate Total Cost of Ownership (TCO)
    Clearly articulate TCO, transparently addressing hidden costs and long-term value. Proactively managing TCO calculations reduces customer uncertainty and enhances market confidence, directly addressing Gartner’s challenges around vendor lock-in perceptions.
  1. Prioritize rigorous and proactive risk management
    Continuously deploy automated vulnerability scanning and remediation, maintain curated internal OSS catalogs, and automate compliance documentation (SBOM/VEX) to streamline audits, minimize risk exposure, and simplify regulatory compliance. Kelly emphasized during her keynote at RSAC 2025, “Rigorous, automated risk management is essential to managing open-source cybersecurity effectively.”

Conclusion: Mastering open source for strategic advantage

For cybersecurity startups, strategically leveraging open-source AI offers unparalleled innovation, differentiation and sustained growth opportunities. Embedding governance deeply, automating security through generative AI, contributing purpose-built community tools, proactively managing total cost of ownership (TCO) and rigorously mitigating risks positions startups as industry leaders capable of driving significant cybersecurity transformation.

As Jeetu Patel summarized at RSAC 2025: “Strategic open-source innovation is essential to collectively securing our digital future. The adversary—not competitors—is our true challenge.”

By embracing these strategic insights, cybersecurity startups can confidently navigate the complexities of open-source software, driving transformative industry leadership and long-term competitive success.

Join me at VB Transform 2025

I’ll be hosting a roundtable focused on this topic, called “Building Cybersecurity Apps with Open Source,” at VentureBeat Transform 2025, happening June 24–25 at Fort Mason in San Francisco. Register and sign up to join me in conversation. Transform is VentureBeat’s annual event bringing together enterprise and AI leaders to discuss practical, real-world AI strategies. 

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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