17 September 2026
Artificial Intelligence Security Testing
As organisations increasingly adopt Artificial Intelligence (AI) an important question we all should be asking is - how do we know whether our AI can be trusted?
While traditional penetration testing remains an essential component of cyber security, AI systems introduce additional risks that extend beyond networks, applications, and infrastructure.
Traditional penetration testing focuses on identifying technical vulnerabilities that could allow attackers to gain unauthorised access to systems or data. Testers look for weaknesses such as software vulnerabilities, configuration errors, poor authentication controls, network security flaws etc.
AI makes cyber security risk registers more important than ever - read more here: AI Amplifies The Importance Of A Cyber Security Risk Register
AI security testing takes a broader view. An AI solution may sit within a secure environment but can still produce harmful, inaccurate, biased, or unauthorised outcomes. AI security testing questions whether someone can manipulate the AI’s intelligence, decision-making, or behaviour.
Why AI Security Testing Requires A New Mindset
Traditional penetration testing focuses on protecting systems and looks to answer the question: "Can someone break into our systems?"
AI security testing aims to answer a different question altogether: "Can someone manipulate our AI system to think, decide, or behave in ways that create risk for our organisation?"
As AI becomes embedded within business processes, organisations will need both testing disciplines working together. Traditional security testing to protect the technology stack, with AI security testing providing the assurance that the intelligence operating within that stack remains secure, reliable, compliant and explainable.
OWASP AI Security Testing Guides And Frameworks
The OWASP Foundation develops resources to provide organisations with a practical foundation for AI governance, secure development, testing, and operational control of LLM-based and autonomous agent systems. The foundation has two informative AI guides:
The OWASP Top 10 for LLM Applications, a widely adopted framework identifying the most critical risks in AI systems
The OWASP Top 10 for Agentic Applications, focused on risks unique to autonomous AI agents
The guides deliver:
A structured list of the highest-priority AI security risks
Real-world attack and misuse scenarios
Recommended security controls and mitigation strategies
Guidance for governance, risk management and secure design
Alignment with recognised frameworks such as NIST, MITRE ATLAS and CWE
A common language for discussing AI risks between executives, technical teams and auditors
Need Help With AI Security Testing?
Organisations that recognise the testing distinction early will be better positioned to realise the benefits of AI while managing the unique risks that accompany it. If you would like to discuss our AI security testing capabilities and services then contact us today.