Securing AI models, APIs, and supply chain
Cybersecurity
Controls
AI introduces a fundamentally new attack surface that traditional cybersecurity programs were not designed to address. Prompt injection, model inversion, data poisoning, and supply chain compromise of third-party AI models are documented, active threat vectors — not theoretical risks. Organizations that treat AI security as a subset of standard IT security are systematically under-protected.
Why It Matters
The consequences of AI-specific security failures are distinct from traditional breaches. A successful prompt injection attack can cause an LLM-powered application to exfiltrate data, bypass access controls, or generate harmful outputs — without triggering any traditional intrusion detection. Poisoned training data can corrupt model behavior in ways that are difficult to detect and harder to remediate. NIST AI RMF explicitly maps adversarial threats across the AI lifecycle. MITRE ATLAS catalogs AI-specific tactics and techniques. Organizations cannot rely on perimeter security to protect AI systems.
Regulatory Context
NIST AI RMF Map 5.2 addresses adversarial attacks as a primary risk category. The EU AI Act requires robustness and cybersecurity controls for high-risk AI systems. CISA's guidance on AI and critical infrastructure identifies AI supply chain risk as a national security concern.
Key Dimensions
The assessment evaluates Cybersecurity Controls across four questions, each targeting a distinct governance dimension:
Controls specifically designed to protect AI models and APIs against prompt injection, model inversion, data poisoning, model extraction, and other AI-specific attack techniques — not just standard application security.
Formal security assessment of AI APIs, vendor models, and supply chain dependencies — including contractual security controls, shared responsibility clarity, and ongoing vendor monitoring.
Logging and continuous monitoring of AI model access and outputs for anomalous behavior, unauthorized usage, and potential security incidents — integrated with the organization's existing incident response processes.
Inclusion of AI systems in the organization's penetration testing and vulnerability management program, with AI-specific test cases reflecting adversarial AI attack scenarios.
What Optimizing Looks Like
An organization scoring in the Optimizing range (76–100) on Cybersecurity Controls demonstrates:
- →Comprehensive adversarial defenses documented, tested, and regularly validated against current threat intelligence
- →Formal vendor security assessments with contractual AI security controls and clear shared responsibility boundaries
- →Continuous monitoring of AI model behavior and outputs, integrated with incident response and anomaly detection
- →AI systems fully included in vulnerability management and pentesting with AI-specific attack scenarios from frameworks like MITRE ATLAS
Assessment Questions
The following four questions make up the Cybersecurity Controls domain in the assessment. Each is scored 0–3, for a maximum of 12 raw points (normalized to 0–100).
Are your AI models and APIs protected against adversarial attacks (e.g., prompt injection, model inversion, data poisoning)?
How are third-party AI models, APIs, and vendor integrations assessed for security risk?
Is AI model access and output audited for security incidents or anomalous behavior?
Are AI systems included in your vulnerability management and penetration testing program?
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See how this domain fits into the full assessment → Assessment Methodology