What should organisations do before using AI to support incident response?

Why This Matters for Security Teams

AI can accelerate incident response, but it can also amplify the wrong assumptions if the identity baseline is incomplete. If account ownership is unclear or stale permissions remain active, an AI assistant may prioritise the wrong users, miss shadow access, or recommend containment actions that disrupt the wrong service. That risk is not theoretical; The 52 NHI Breaches Report shows how often compromised identities become the practical entry point for larger incidents.

Before AI is asked to assist, teams need a current view of human and non-human access, active privileges, and ownership boundaries. That is especially important when AI is being used to summarise alerts, suggest containment, or correlate suspicious activity across systems. As Anthropic has documented in its report on an AI-orchestrated cyber espionage campaign, autonomous assistance can accelerate attacker tradecraft as well as defender workflow if the underlying inputs are wrong. In practice, many security teams encounter identity confusion only after containment decisions have already been made under pressure, rather than through intentional pre-incident preparation.

How It Works in Practice

The safest pattern is to treat identity reconciliation as a prerequisite for AI-assisted response, not a cleanup task after the fact. Start with a verified inventory of who owns each account, what permissions are still active, which secrets and tokens are associated with those identities, and which non-human identities are actually in use. Then feed the AI only the curated response context, not raw logs alone. That keeps the model from over-weighting noisy indicators while missing the access path that matters most.

Practically, this means pairing incident response playbooks with identity data from IAM, PAM, directory services, cloud control planes, and secrets inventories. The AI should be constrained to explain its recommendation in terms of known identities, active entitlements, and recent authentication events. It should not be allowed to infer ownership from naming conventions or stale ticket metadata. The State of Secrets in AppSec findings are a useful reminder that secrets sprawl and fragmented tooling often undermine that baseline. For teams building formal response workflows, the NIST guidance on AI Risk Management Framework supports governance, mapping, and measurement before deployment.

• Reconcile account ownership across human, service, and machine identities.
• Identify active permissions, especially privileged and dormant entitlements.
• Classify which secrets, tokens, and certificates are still valid.
• Limit the AI to approved response inputs and explicit decision support roles.
• Require human approval for destructive actions such as disablement or isolation.

This guidance tends to break down in environments with multiple identity silos and no authoritative source of ownership, because the AI will inherit the same fragmentation that already exists in the organisation.

Common Variations and Edge Cases

Tighter pre-incident identity reconciliation often increases operational overhead, requiring organisations to balance response speed against the cost of maintaining an accurate baseline. That tradeoff is real, especially in cloud-first environments where service accounts, temporary credentials, and automated workloads change frequently.

Best practice is evolving for AI-assisted incident response in these cases. Some teams can maintain a near-real-time identity graph; others must rely on periodic reconciliation and strict scoping rules. The right threshold depends on how autonomous the AI is allowed to be. If the tool only drafts analyst notes, the baseline can be narrower. If it can recommend isolation, token revocation, or account disablement, the identity source of truth must be much stronger. For broader NHI context, Ultimate Guide to NHIs — Why NHI Security Matters Now is a useful reference, and the question of whether AI should infer identity relationships from behavioural signals remains an area where there is no universal standard yet.

Where incident response teams operate across mergers, third-party integrations, or legacy directories, the main failure mode is stale ownership data. In those environments, AI should be treated as an analyst accelerator, not an authority on access control.

Related resources from NHI Mgmt Group

• Why is NHI ownership attribution important for incident response?
• How do organisations make AI agent visibility useful for compliance and incident response?
• How should security teams use attribution in incident response?
• How should security teams reduce incident response time with centralized authorization?