AI Controls Matrix

A control framework that translates AI security and governance requirements into concrete, checkable objectives. In practice, it helps organisations map risk management goals to operational controls across identity, access, logging, and compliance. For agentic systems, it is useful when deciding which controls are implemented by the platform and which remain with the enterprise.

Expanded Definition

An AI Controls Matrix is a control mapping tool that turns high-level AI security, privacy, and governance requirements into auditable operational checks. In NHI and agentic ai environments, it helps teams decide which safeguards belong to the platform, which belong to the enterprise, and where shared responsibility begins and ends. The concept is still evolving across vendors and frameworks, so the matrix should be treated as a living control model rather than a fixed standard.

Practically, the matrix often spans identity assurance, privileged access, logging, data handling, human approval gates, incident response, and model lifecycle controls. That makes it closely related to control catalogs such as NIST Cyber AI Profile (IR 8596), but the matrix is more operational: it translates requirements into tests, owners, and evidence. NHIMG’s Ultimate Guide to NHIs — Standards is useful context for how identity-centric controls are expected to support machine agents and service identities.

The most common misapplication is treating the matrix as a policy document only, which occurs when teams list controls without assigning ownership, evidence, or a verification method.

Examples and Use Cases

Implementing an AI Controls Matrix rigorously often introduces governance overhead, requiring organisations to weigh faster AI delivery against stronger assurance, clearer accountability, and slower approval cycles.

• Mapping an agent’s tool access to explicit approval, logging, and revocation controls so the enterprise can prove who authorised what and when.
• Separating platform-managed controls, such as runtime isolation or model guardrails, from enterprise-managed controls such as data classification and exception approval.
• Using the matrix to assess whether service identities, API keys, and secrets are covered by the same review cadence as human privileged accounts, especially after patterns seen in the LLMjacking research.
• Aligning control objectives to NIST guidance such as NIST Cyber AI Profile (IR 8596) when defining logging, monitoring, and incident handling expectations for AI systems.
• Using a matrix to compare multiple AI use cases, such as internal assistants, customer-facing copilots, and autonomous remediation agents, without applying the same control set blindly to each one.

NHIMG research on the DeepSeek breach shows why this matters when AI systems are exposed to secrets, credentials, and sensitive records.

Why It Matters in NHI Security

AI controls fail most often at the boundary between model behavior and identity governance. If an organisation cannot show which controls apply to an AI agent, a workload identity, or a secret-backed integration, then the system may appear governed while still being overprivileged, underlogged, or impossible to investigate. This is especially important in NHI security because machine identities often operate continuously, at scale, and with far less human oversight than traditional user accounts.

NHIMG’s State of Secrets in AppSec research highlights the scale of the problem: organisations maintain an average of 6 distinct secrets manager instances, which fragments control ownership and weakens centralised oversight. An AI Controls Matrix helps reduce that fragmentation by forcing explicit answers about where secrets live, who can rotate them, and what evidence proves control operation. The matrix also supports alignment with NIST Cyber AI Profile (IR 8596) when AI risk moves from theory into operational review.

Organisations typically encounter the need for an AI Controls Matrix only after an agent misuses a secret, logs are insufficient for investigation, or a regulator asks who owned the failed control, at which point the matrix becomes operationally unavoidable to address.

Related resources from NHI Mgmt Group

• What NHI security controls are mandatory for autonomous Agentic AI?
• What governance controls should every enterprise put in place before deploying AI agents?
• What are the emerging security controls needed for Agentic AI identity governance?
• Should organisations build separate controls for AI agent deployments?