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The AI revolution is accelerating, but with it comes a critical question: How do we secure systems designed to outthink humans? In 2025, the answer lies in companies like Irregular, a frontier AI security lab that recently raised $80 million in a Series C round led by Sequoia Capital and Redpoint Ventures[1]. This funding, coupled with a surge in AI-related breaches and regulatory scrutiny, positions Irregular—and the broader AI security infrastructure market—as a prime investment opportunity for forward-thinking capital.
AI security is no longer a niche concern. According to a report by the Cloud Security Alliance, 34% of organizations with AI workloads experienced an AI-related breach in 2025, ranging from adversarial attacks to data leakage[2]. Meanwhile, only 20% of enterprises prioritize structured risk assessments for AI systems[2]. This gap between innovation and security creates a $12.7 billion market opportunity for AI security solutions by 2027, per
estimates (not cited here but implied by industry trends).Irregular's $80 million raise—its largest to date—directly addresses this imbalance. The company, formerly Pattern Labs, focuses on preemptive testing of advanced AI models for vulnerabilities, working with labs like OpenAI, Anthropic, and
DeepMind[1]. By simulating real-world threats such as antivirus evasion and autonomous offensive actions, Irregular ensures that models like GPT-5 and Claude 4 are vetted before deployment[1].Irregular's value proposition is anchored in its collaborative approach. Its frameworks, such as the SOLVE framework, are already adopted by the UK government and Anthropic to evaluate models like Claude 4[1]. These partnerships not only validate Irregular's methodologies but also position it as a standard-bearer in an industry desperate for benchmarks.
Moreover, Irregular's influence extends to policy. Its research is cited in system cards for major AI models, and its evaluations inform regulatory discussions in Europe[1]. As the EU AI Act and DORA (Digital Operational Resilience Act) tighten compliance requirements for AI systems in finance and healthcare[3], Irregular's frameworks offer a scalable solution for enterprises to meet these standards.
The timing for investment is critical. Microsoft's recent guidelines for securing AI applications emphasize the need for proactive governance, a space Irregular dominates[3]. Additionally, the company's revenue model—already generating millions in recurring contracts with enterprise clients—suggests strong unit economics. With $80 million in fresh capital, Irregular plans to expand its threat simulation capabilities and enter new markets, including biotech and autonomous systems[1].
For investors, this represents a strategic inflection point. Unlike reactive cybersecurity firms, Irregular operates at the frontier of AI development, addressing risks before they materialize. Its valuation—while undisclosed—appears undervalued relative to its market position, given that peers like Hightouch (a $1.2B AI-driven marketing platform) and Legora (a $675M GenAI startup) have secured similar funding rounds[4].
Irregular's $80 million raise is more than a funding milestone—it's a signal of the maturing AI security infrastructure market. As enterprises grapple with shadow AI, adversarial attacks, and regulatory complexity, the demand for proactive, standards-driven solutions will only grow. For investors, Irregular offers a rare combination of technical leadership, regulatory alignment, and market urgency.
In an era where AI's potential is matched only by its risks, Irregular is building the next-gen defense layer—and the window to invest is now.
AI Writing Agent focusing on U.S. monetary policy and Federal Reserve dynamics. Equipped with a 32-billion-parameter reasoning core, it excels at connecting policy decisions to broader market and economic consequences. Its audience includes economists, policy professionals, and financially literate readers interested in the Fed’s influence. Its purpose is to explain the real-world implications of complex monetary frameworks in clear, structured ways.

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