Over-Reliance On AI

Over-reliance on AI occurs when users or systems trust model output too much, treating it as authoritative even when it should be challenged or reviewed. In security programmes, this can turn advisory output into an operational decision point without sufficient validation.

Expanded Definition

Over-reliance on AI describes a decision-making failure mode where people or automated workflows accept model output with too little scrutiny, especially when the output is persuasive, fast, or embedded into routine operations. In security settings, the issue is not that AI is used, but that its advice is treated as a substitute for analysis, verification, and accountability. The concept applies across detection, triage, prioritisation, code generation, identity review, and agentic workflows where an AI system has execution authority.

Definitions vary across vendors and product categories, but the governance concern is consistent: the human or control plane assumes the model is correct because it sounds confident or because it has been integrated into a trusted workflow. NIST’s control guidance in NIST SP 800-53 Rev 5 Security and Privacy Controls is relevant because it reinforces the need for review, accountability, and control over system actions. Over-reliance is especially risky when AI is used to recommend privileged actions, approve identities, or trigger automated response without independent validation.

The most common misapplication is assuming that a high-confidence answer is a verified answer, which occurs when teams suppress manual review after AI output becomes operationally convenient.

Examples and Use Cases

Implementing AI-assisted workflows rigorously often introduces latency and review overhead, requiring organisations to weigh speed and scale against the cost of human validation.

• A SOC analyst accepts an AI-generated incident summary and closes the case without checking raw telemetry, allowing a real intrusion to continue unnoticed.
• A security team uses AI to prioritise alerts, but repeated false confidence causes low-signal recommendations to outrank verified detections from NIST-aligned control reviews.
• An IAM workflow allows an AI assistant to recommend access grants, and the approver rubber-stamps the suggestion without reviewing role fit, business need, or separation-of-duties impact.
• A developer uses AI-generated remediation steps in production change management, but the output is not tested against current dependencies or rollback constraints.
• An agentic ai system is allowed to trigger containment actions, yet no checkpoint exists for high-impact decisions, so the system over-acts on a benign event and disrupts service.

These use cases show why over-reliance is a governance problem, not just a usability problem. The issue is not whether the model is useful, but whether the organisation has designed enough friction to catch model error before it becomes an operational decision. For broader AI risk framing, NIST’s AI Risk Management Framework is useful for structuring oversight and measuring when human review is still required.

Why It Matters for Security Teams

Security teams are particularly exposed because AI is often introduced to reduce analyst workload, accelerate triage, and standardise decisions under pressure. Those are legitimate benefits, but they also create a path for automation bias, where defenders trust the machine because it has been right often enough to feel dependable. Once that happens, false negatives, bad prioritisation, and inappropriate access decisions can propagate quickly through SIEM, SOAR, IAM, and agentic workflows.

For identity and access operations, over-reliance can turn AI-generated recommendations into de facto approvals, weakening segregation of duties and increasing the chance of unauthorised access. For governance, the risk is that no one can explain why the system acted, who checked it, or which control was bypassed. NIST’s digital identity guidance in NIST SP 800-63 Digital Identity Guidelines is relevant where AI touches identity assurance, because identity decisions must remain tied to assurance, not convenience. The OWASP Agentic AI Top 10 also highlights the danger of granting autonomous systems too much authority without guardrails.

Organisations typically encounter the consequences only after a mistaken model recommendation has been executed, at which point over-reliance on AI becomes operationally unavoidable to address.

Related resources from NHI Mgmt Group

• Why do AI agents increase the blast radius of over-scoped NHI tokens?
• How can organisations reduce unsafe AI outputs without over-restricting users?
• When should organisations prioritize passwordless authentication over broader AI automation?
• What breaks when AI agents inherit over-privileged cloud roles?