What happened
Leading security standards like NIST CSF, ISO 27001, and CIS Controls are proving inadequate for defending against AI‑specific attack vectors. These frameworks were developed for traditional IT assets and don’t cover novel threats such as prompt injection, model poisoning, or AI supply chain compromise. As a result, organizations that meet compliance requirements can still be breached through AI‑centric vulnerabilities.
Who is affected
Enterprises and security teams deploying AI/ML systems across applications, from chatbots to code assistants and predictive analytics, are most at risk. Even organizations with mature security programs and comprehensive controls remain exposed because current frameworks lack guidance for these new threat surfaces.
Why CISOs should care
AI adoption is expanding rapidly, yet the attack landscape has evolved faster than the frameworks meant to secure it. Traditional controls can miss semantic threats embedded in natural language or authorized workflows like model training. Without AI‑aware defenses, organizations risk breaches, data leakage, regulatory penalties, and operational disruption, even while claiming compliance.
3 practical actions:
- Conduct an AI‑specific risk assessment separate from standard security reviews to identify blind spots in AI systems and their data pipelines.
Integrate AI‑centric controls such as prompt monitoring, model integrity verification, and adversarial robustness testing into your security stack ahead of framework updates. - Build internal AI security expertise by training or hiring specialists who understand AI attack vectors and can update incident response plans accordingly.