The identity-first guide to securing AI agents

AI agents are already working inside your organisation. They call tools, reach business systems and act on your behalf at machine speed. Agentic AI security makes sure every one of them is known, owned, securely connected, narrowly authorised and observable, so you can scale autonomy without accepting unmanaged risk.

ProofID and Coleman Parkes, 2027 State of Agentic AI Readiness, survey of 300 enterprise decision-makers in the US, UK and Europe

What is agentic AI security?

Agentic AI security is the set of identity, access and monitoring controls that keep AI agents within the boundaries you've set. It answers five questions for every agent: does it exist, who owns it, how does it connect, what is it allowed to do right now, and can you step in when it goes wrong?

Agentic AI refers to systems that make decisions and take action to achieve a goal, including calling tools, accessing business systems and completing workflows. That excludes generative tools that only respond to prompts. The difference matters: a chatbot that drafts an email is a content risk, while an agent that sends it, updates the CRM and books the follow-up is an access risk.

That's why agentic AI security is, at its core, an identity problem. Traditional AI security focuses on the model: prompt injection, data leakage, poisoned training data. Those risks are real. But once an agent can act, the question that decides your exposure is what it can reach and who authorised it.

Why do AI agents need their own identity?

AI agents need their own identity because they act with access, and access without attribution can't be governed. An AI agent is an identity and needs the same discipline as any human user: a known owner, least-privilege permissions, periodic review and a way to revoke it.

The principles aren't new. Identity and access management has spent years solving the same problems for people, applications and workloads. What agentic AI changes is speed, scale and autonomy.

• Speed. Humans are slow and selective in how they use their access. An agent can exercise every permission available to it, thousands of times faster.
• Autonomy. Unlike a conventional API, an agent chooses between tools and actions as it pursues a goal. You can't predict every path in advance.
• Inherited access. When an agent runs under a developer account or a shared credential, it can carry years of accumulated permissions it was never meant to have.
• Attribution. Without explicit delegation, an agent acting for a user looks identical to the user. Investigation and proof of control both get harder.

This is why weak permissions that were tolerable for a human can become a much bigger problem for an agent. A relatively simple agent with broad access can still do serious damage.

How ready are organisations to secure AI agents?

Most organisations aren't ready. ProofID's 2027 research with Coleman Parkes found that adoption is widespread, but the visibility, attribution and response capabilities needed to govern it are not. Five readiness gaps stand out.

Regional differences are modest: US and EMEA respondents are almost identical on several foundational measures. The bigger variation is by sector.

See where you sit against these benchmarks: Take the readiness assessment

The five pillars of agentic AI security

ProofID's five pillars, Discover, Govern, Connect, Trust and Observe, extend established identity controls to AI agents. They work as one control chain, not five separate projects: you can't govern an agent you haven't discovered, apply reliable policy without a unique identity, or contain an agent quickly if its connection can't be isolated.

Discover: how do you find every AI agent in your organisation?

You find every AI agent by reconciling several discovery signals into one register, then keeping it current. No single method shows the whole estate. Browser telemetry misses desktop apps, personal devices and direct API connections.

Useful signals include identity systems, cloud and agent-platform inventories, network ingress and egress monitoring, endpoint management, browser telemetry and gateway logs. Together they build a register showing what each agent does, who owns it, where it runs, what it can reach and how it authenticates.

Discovery is also cultural. Give people a clear route to request, test and register AI tools. Overly restrictive policy pushes activity into personal accounts, which makes the estate harder to see.

Govern: who is accountable for an AI agent?

Every AI agent needs a named human owner who stays accountable for its purpose, autonomy, access and continued operation. Security, IT, AI and risk teams all contribute, but shared responsibility still needs one clear point of accountability.

Ownership has to follow the agent as it changes. Register and risk-classify agents before production access. Trigger a review when tools, models, data or purpose change materially. Retire the identity and its connections when the use case ends.

Human-in-the-loop control should be targeted, not applied to every action. Risk tiers let routine activity run inside defined boundaries, while unusual or high-consequence actions trigger approval or a block.

"The goal is to put human judgement where it adds value without turning autonomy into a queue." Matthew Berdine, SVP of Client Solutions and Architecture, ProofID

Connect: what is an AI gateway or MCP gateway?

An AI or MCP gateway is a central control point that agent traffic passes through on its way to models, tools and data. Model Context Protocol (MCP) gives agents a standard way to discover and call tools. An MCP gateway centralises those tools, while an AI gateway focuses on control: authentication, rate limits, token handling, policy enforcement, audit and the ability to block a connection.

Without a standard pattern, direct connections multiply, each with its own identity model, token scope, logging and revocation path. That creates control debt. During an incident, teams end up choosing between leaving a risky route open and disrupting legitimate services.

A gateway won't fix a shared identity or weak access policy. It gives those controls a consistent place to operate.

Trust: how do you apply least privilege to AI agents?

You apply least privilege to AI agents by giving each one its own identity, then granting access that is both short-lived and narrowly scoped to the task. Expiry on its own isn't least privilege. A one-hour token can still let an agent search several systems, retrieve sensitive data or change records for the whole hour.

The control model has to reconcile three sources of authority: the team that built the agent, the task it's performing and the user or service it represents. A scheduling agent should be able to change the current user's calendar without inheriting authority over every calendar the underlying service can reach.

Zero standing privilege means the agent keeps no permanent access it doesn't need. Just-in-time access grants permission for the task and removes it afterwards. Runtime policy then allows normal actions, triggers human approval for unusual ones, or blocks the request.

Observe: how do you stop a rogue AI agent?

You stop a rogue AI agent with a graduated response that targets that one agent, not a blunt kill switch. Depending on severity, you might slow how quickly it can act, remove a permission, block a tool connection, require human validation, revoke its identity or terminate the process.

That only works if the earlier pillars are in place. A unique identity and a central gateway let you isolate one agent instead of disabling a human user or every service sharing a credential. A kill switch is the last line of defence, not the control model.

Useful observation records what the agent did, not why it might have done it: the identity used, who it represented, the tool or API called, the data reached, the policy decision and the outcome. Those records become your audit trail for regulators and customers.

"You cannot watch the mind, so you have to watch the hands." - Neil Evans, Chief AI Officer, ProofID

 

Where should you start with agentic AI security?
Start with visibility, narrow access and a route to intervene. The sequence below improves control without asking your organisation to pause every AI initiative.

1. Make the estate visible and accountable. Combine the discovery signals you already have into one agent register. Prioritise agents that can change system state, reach sensitive data or act for customers. Assign each a named owner and document its purpose, connections and current permissions.
2. Standardise connections and narrow access. Define an approved integration pattern for new agents and move the highest-risk existing connections behind a controllable gateway. Give each agent a unique identity, separate its authority from the builder's and user's standing access, and scope permissions to the current task.
3. Instrument behaviour and build a graduated response. Record who or what acted, on whose behalf, against which resource and with what result. Connect alerts to a response that can reduce scope, block a route, request validation or revoke the identity. Test that response before you need it.

"The right time to do governance is before deployment, not after. If you're doing it afterwards, it's too late and you're always playing catch-up." Tom Eggleston, CEO, ProofID

People, process and technology need to move together. Shadow AI is partly a technical discovery problem, but it can also be a sign that teams don't have a workable route to adopt the tools they need.

How does agentic AI readiness vary by sector?

Industry shapes maturity more than geography does. Banking leads on production adoption and control infrastructure, while Government and Education are earlier on both.

How ProofID helps you secure agentic AI

ProofID is a pure-play identity security partner. We've been 100% focused on identity since 2014, securing both human and non-human identities throughout. Our agentic AI identity security solutions extend that work to AI agents, built on the identity platforms you already run.

• Agentic AI Enablement. An advisory engagement that applies the five pillars to your environment: an executive briefing, a discovery workshop, a security gap analysis and a board-ready architecture roadmap.
• Professional Implementation. Certified engineers who deploy agent identity, authorisation and gateway controls across Ping Identity, SailPoint and Palo Alto Networks.
• Run and Support. Managed Identity Services and the Identity Security Squad keep agent registers, certifications and controls current as your estate grows.

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identities protected daily

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Ping Identity Global Delivery Partner of the Year

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years of proven delivery excellence

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identities protected daily

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dentity projects delivered

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Ping Identity Global Delivery Partner of the Year

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years of proven delivery excellence

Agentic AI security glossary

How ready are your AI agents?

Take our short assessment to see how your controls compare with the 300 organisations in our research. Then talk through your results and next steps with one of our experts.