Is an AI agent a non-human identity?

Yes. Security teams classify AI agents, along with service accounts, API keys, and workload identities, as non-human identities (NHIs). The difference is autonomy: a service account executes predefined instructions, while an agent interprets goals and chooses actions. That’s why agents need the fuller governance treatment described above, ownership, scoped permissions, and lifecycle management, not just credential hygiene.

Who is accountable when an AI agent does something wrong?

A named human, always. Every agent needs a business owner (accountable for what it’s used for) and a technical owner (accountable for how it runs). If no one can answer “whose agent is this?” during an incident, the governance program has already failed.

How is governing an AI agent different from governing a service account?

Service accounts are static: fixed credentials, predictable behavior, and quarterly review. Agents acquire permissions dynamically, chain actions across systems, and change behavior as capabilities are added, so point-in-time reviews miss most of what matters. Governance has to move to runtime: continuous monitoring of what the agent actually did, not periodic checks of what it could do.

What evidence will an auditor expect for AI agent oversight?

Expect requests for agent inventory, ownership records, permission approvals and reviews, activity logs, and proof that human-approval gates fired for high-risk actions. Frameworks are catching up fast; ISO 42001, the AI management system standard, formalizes several of these expectations.

AI Governance Archives

AI Governance

The Rise of Non-Human Identities: Why AI Agents Need Identity Governance

TL;DR AI agents now read your email, query your databases, update your CRM, and trigger workflows. This is the kind of access you’d normally reserve for privileged users. Most organizations still govern them like regular applications. Machine identities already outnumber human identities by more than 80-to-1 (CyberArk, 2025), and the consequences are evident: 30% of…

AI Governance

3 Reasons Your AI Governance Stack Needs to Be Always-On

You’ve built the governance program. You have selected and customized a framework, set up the controls, and assigned owners. Most GRC and TPRM teams have. In fact, 25% of organizations describe their AI Governance as advanced, and a majority have dedicated budgets for AI governance. If that sounds familiar, you’ve made good progress. But there…

AI Governance

How to Build an AI Governance Stack That Runs Continuously

TL;DR Most organizations already have, or are building, AI Governance in some form. Precisely 69.9% as per Sprinto’s CISO Pulse Check Report released earlier this year. Moreover, only 52.81% of GRC teams even track AI as a separate category, let alone ensure AI risk is being governed continuously. But what happens to AI Governance when…

Blog banner image summarising dependency and runtime risks in vendor ecosystems

AI Governance

How to rethink TPRM architecture for agentic, runtime, and AI dependency risks

TPRM has always been about understanding who your key vendors are, what access they hold, and safeguarding your business against the breaches, downtime, and disruption that could follow if they fail. A vendor-side diligence model built for a world where third-party risk was largely static. In an AI third-party risk age, that model is no…

Blog cover depicting 7 AI lessons across prompt injection, agentic AI, input risk, supply chain, downtime, 0Auth tokens and marketplace risk

AI Governance, Third-party Risk Management

7 Real AI Risk Incidents in 2025-26, and the Control Gaps They Exposed

TL;DR – This article looks at seven incidents that happened in the last 18 months, and the specific controls that may have caught or prevented them– The failures weren’t sophisticated: misconfigured vendors, unscoped agents, unmapped dependencies, and LLM outages that took business workflows down with no continuity plan in sight– The programs that avoid incidents…

Infographic comparing the building blocks and consequences of banning vs governing AI

AI Governance, Blogs

5 AI Governance Strategies That Don’t Block Teams: The Practitioner Playbook

TL;DR – AI governance fails when it’s too loose to catch anything or too tight to let teams move– The answer is making the safe path faster than the workaround, not blocking the workaround– Classify by data type and destination, enforce at the point of exposure, log everything Imagine data leaving the environment through unvetted…