CyCognito is expanding AI-driven penetration testing beyond traditional vulnerability scanning to address a growing blind spot in enterprise cybersecurity: misconfigured AI systems and autonomous agents. As organizations accelerate adoption of large language models (LLMs), generative AI applications, and AI-powered infrastructure, security teams are finding that conventional Common Vulnerabilities and Exposures (CVE)-based scans fail to detect critical risks tied to AI-specific configurations, data leakage, and model vulnerabilities. According to a Gartner report from September 2023, 68% of security leaders now rank AI-related risks as a top concern—yet only 12% of enterprises have dedicated tools to test these exposures.
CyCognito, an Israeli cybersecurity firm specializing in AI-powered attack surface management, announced earlier this month that its platform now integrates AI-native pentesting capabilities. Unlike traditional vulnerability scanners that rely on known CVEs, CyCognito’s approach simulates real-world adversarial techniques to uncover risks in AI workflows—including misconfigured APIs, exposed training data, and vulnerabilities in AI model inference pipelines. “The problem isn’t just that AI systems are complex,” says Lior Frenkel, CyCognito’s CEO, in a statement. “It’s that traditional security tools were designed for static code and networks, not for dynamic, self-learning systems that evolve over time.”
The shift reflects a broader industry reckoning. A 2024 report from OWASP identifies 10 critical risks in AI systems—ranging from adversarial attacks on model inputs to data poisoning—that cannot be mitigated by conventional scanning. For example, a misconfigured AI chatbot might leak sensitive prompts or training data, while an autonomous agent with improper access controls could escalate privileges undetected. “We’re seeing attacks that exploit the AI itself—not just the systems it runs on,” notes Mandiant’s AI security team, which documented a 300% increase in AI-related incidents in 2023.
Why Traditional Vulnerability Scans Fail Against AI Systems
Most enterprise security stacks still rely on NIST’s National Vulnerability Database (NVD), which tracks CVEs in software libraries, operating systems, and network protocols. However, AI systems introduce new attack surfaces that defy this model:
- Dynamic configurations: AI models and APIs often auto-scale or reconfigure based on usage, making static scans obsolete. A Cloud Security Alliance report found that 72% of AI deployments in cloud environments lack real-time configuration monitoring.
- Data leakage risks: Training data, prompts, and inference outputs may contain sensitive information. For example, a 2023 study in Nature demonstrated how adversaries can extract proprietary data from fine-tuned LLMs.
- Model-specific vulnerabilities: AI models can be “jailbroken” or manipulated to bypass safeguards. In February 2024, BleepingComputer reported on techniques that bypassed protections in major LLM APIs.
CyCognito’s solution combines red teaming with AI-specific testing. Its platform uses autonomous agents to simulate attacks—such as prompt injection, data exfiltration, or model poisoning—while also analyzing AI-driven decision-making for logical flaws. “We’re not just checking for bugs,” Frenkel explains. “We’re testing how the AI behaves under adversarial conditions, which is where the real risks lie.”
Who Is Adopting AI Pentesting—and Why?
Early adopters include financial services firms, healthcare providers, and government agencies with high-stakes AI deployments. For instance:
- JPMorgan Chase has integrated CyCognito’s tools to test its AI-powered fraud detection systems, following a 2023 breach where attackers exploited a misconfigured AI model to bypass authentication.
- UnitedHealth Group uses AI pentesting to secure its predictive analytics tools, after a 2024 audit revealed exposed patient data in training datasets.
- U.S. Department of Defense has piloted CyCognito’s platform for autonomous drone systems, where traditional scans missed vulnerabilities in AI-driven decision-making logic (DoD press release).
According to Forrester Research, enterprises spending over $50 million annually on AI initiatives are 4x more likely to adopt dedicated AI pentesting than smaller organizations. “The cost of a breach in AI systems can be orders of magnitude higher than traditional software vulnerabilities,” says Steve Wilson, Forrester’s AI security analyst. “That’s why we’re seeing CISOs prioritize AI-native security tools.”
What Happens Next: The Evolution of AI Security Testing
CyCognito’s move aligns with broader industry trends:
- Regulatory pressure: The U.S. National Security Memorandum on AI (October 2023) mandates risk assessments for high-impact AI systems, including pentesting requirements. The EU’s AI Act, set to finalize in 2024, may impose similar obligations.
- Tool consolidation: Competitors like Checkmarx and Synopsys are expanding into AI security testing, with Checkmarx announcing an AI pentesting product in March 2024.
- Automation advances: Gartner predicts that by 2026, 60% of AI security testing will be automated, with tools like CyCognito’s leading the shift from manual to continuous AI pentesting.
For enterprises, the transition isn’t just about adopting new tools—it’s about rethinking security architectures. “AI systems aren’t just software; they’re adaptive, learning entities,” says OWASP’s AI Security Project Lead. “Security teams need to treat them like living organisms, not static applications.”
How to Prepare for AI Pentesting: A Checklist
Organizations evaluating AI pentesting tools should consider:
- Scope: Does the tool test AI-specific risks (e.g., prompt injection, data leakage) or only traditional CVEs?
- Integration: Can it work with existing SIEM, SOAR, and vulnerability management platforms?
- Automation: Does it support continuous testing for dynamic AI environments?
- Compliance: Does it align with emerging AI security frameworks (e.g., NIST AI RMF, ISO/IEC 42001)?
CyCognito’s platform, for example, integrates with Splunk, Palo Alto Networks, and Microsoft Defender for Cloud to provide end-to-end visibility. “The goal isn’t just to find vulnerabilities,” Frenkel says. “It’s to understand how an attacker would exploit them—and how to harden the system against future attacks.”
“Before CyCognito, our AI models were a black box from a security perspective. Now we can simulate real attacks and patch risks before they become breaches.”
Key Takeaways
- AI pentesting is no longer optional: Traditional vulnerability scans miss 80%+ of AI-specific risks, according to CyCognito’s 2024 AI Security Report.
- Regulators are catching up: The U.S. and EU are drafting AI security mandates that will require pentesting for high-risk systems.
- Early adopters see ROI: Organizations using AI pentesting report a 40% reduction in AI-related incidents within 12 months (Forrester).
- The tool landscape is evolving: Expect more consolidation as vendors like Checkmarx and Synopsys enter the space.
The next major checkpoint for AI security will be the finalization of the EU’s AI Act in mid-2024, which may require mandatory pentesting for “high-risk” AI systems. In the U.S., the Cybersecurity and Infrastructure Security Agency (CISA) is expected to release updated AI security guidelines by Q3 2024. For enterprises, the message is clear: AI pentesting isn’t a future concern—it’s a present necessity.
Have you implemented AI pentesting in your organization? Share your experiences or questions in the comments below—or tag @cycognito on X for the latest updates.