Why AI Agents Are the Most Dangerous PAM Problem You Are Not Solving

Privileged Access Management exists because some accounts matter more than others. Domain administrator credentials, database root accounts, cloud infrastructure keys, secrets management vaults these are the assets that, in the wrong hands, can bring an organisation to its knees. PAM was built to wrap controls around those assets: vaulting credentials, brokering sessions, enforcing least privilege, recording activity.

The discipline has matured significantly over the past decade. CyberArk, BeyondTrust, Delinea, and their competitors have built sophisticated platforms capable of managing privileged access at enterprise scale. Organisations have invested heavily in deployment programmes, and those programmes have delivered real security improvement.

​

But there is a hole in almost every PAM deployment that is widening by the week. AI agents, autonomous, software-based actors that operate using credentials with privileged access are accumulating inside enterprise environments faster than PAM governance frameworks can adapt. And unlike the human administrators that PAM was designed to control, AI agents do not sleep, do not get bored, and do not make the subtle behavioural mistakes that security monitoring tools are calibrated to detect.

​

Privileged Credentials in the Hands of Machines

​

To understand why this matters, it helps to be specific about what AI agents actually do. An enterprise AI agent might be tasked with monitoring infrastructure, analysing security logs, managing cloud resource provisioning, or orchestrating data pipelines. To perform these functions, the agent needs credentials. And the credentials it needs are, by definition, privileged.

​

A DevOps agent provisioning cloud infrastructure requires administrative access to cloud accounts. A security agent monitoring endpoint activity requires access to endpoint management tools. A data pipeline agent moving sensitive information between systems requires elevated database permissions. These are not low-privilege service accounts. They are, in functional terms, privileged identities and they are operating autonomously, at machine speed, with minimal oversight.

​

This creates a direct collision with the core objectives of any PAM programme. PAM is designed to ensure that privileged access is used only by authorised individuals, for defined purposes, within observable sessions, with credentials that are vaulted and rotated regularly. AI agents violate almost every one of these assumptions. They are not individuals. Their "purposes" may evolve dynamically based on the tasks they are assigned. Their sessions are not interactive in the traditional sense. And their credentials — typically long-lived API keys or service account tokens — are frequently neither vaulted nor rotated.

​

The Numbers Make Uncomfortable Reading

​

The scale of the exposure is not hypothetical. According to the 2024 IBM Cost of a Data Breach Report, the average cost of a data breach globally is $4.88 million (approximately £3.85 million). The 2025 Verizon DBIR identifies credential abuse as the most common initial access vector in breaches — meaning that in the majority of significant security incidents, an attacker obtained or exploited a set of credentials. Privileged credentials, by definition, provide the highest-value access once compromised.

​

A 2025 report from Varonis found that organisations average 15,000 stale-but-enabled accounts with over 31,000 stale permissions. Every AI agent deployed adds a non-human identity to this already sprawling attack surface — and unlike the stale human accounts that security teams have at least some processes to identify, AI agent identities are typically invisible to existing access review tooling.

​

Perhaps most striking is a figure from Microsoft's 2024 State of Multicloud Security Risk Report: of 209 million cloud identities analysed, over 50% were "super identities" — accounts with unrestricted access to all resources across cloud estates. These are the accounts that attackers target above all others. As AI agents proliferate in cloud environments, the proportion of super identities is likely to grow, not shrink.

​

The Prompt Injection Problem

​

One of the most serious and least-discussed risks sits at the intersection of AI agent capability and privileged access: prompt injection attacks.

​

A prompt injection occurs when a malicious actor embeds instructions within content that an AI agent will process a document, an email, a web page, a code comment causing the agent to behave in ways its operators did not intend. Because AI agents are designed to follow instructions, a sufficiently crafted injected prompt can redirect an agent's actions entirely.

​

In 2024, attackers embedded hidden instructions in email content that caused an AI assistant at a major financial institution to approve fraudulent wire transfers totalling $2.3 million. The agent had privileged access to transaction approval workflows. The attack exploited that access without ever touching a human credential (Obsidian Security, 2024).

​

This is the specific danger of combining agentic AI with privileged access: the attack surface is not just the credential. It is every piece of content the agent processes. A PAM programme that vaults and rotates credentials but does not control what inputs an AI agent can receive has addressed only part of the problem.

​

What PAM Programmes Need to Do Differently

​

Securing AI agents within a PAM context requires extending the programme's scope in several specific directions.

Credential vaulting must cover non-human identities. Every API key, service account token, and machine credential used by an AI agent should be stored in the vault, subject to rotation policies, and accessible only through the broker — not hardcoded into agent configurations or stored in environment variables.

Session monitoring must adapt to machine behaviour. Traditional PAM session recording captures human keystrokes and mouse movements. AI agent activity does not look like this. Monitoring solutions must be capable of capturing API call patterns, data access sequences, and inter-system communications at the speed agents operate. Behavioural analytics tuned for human activity will miss anomalous agent behaviour entirely.

​

Least privilege must be enforced strictly, not aspirationally. The temptation when deploying AI agents is to grant broad access to ensure they can function across scenarios. This approach must be resisted. Every agent should have a precisely scoped set of permissions, reviewed at deployment and on a regular cadence thereafter. Just-in-Time access models where privileged access is granted for a specific task and expires automatically are particularly well-suited to AI agent use cases.

​

Input controls matter as much as access controls. Given the prompt injection threat, PAM governance for AI agents must extend beyond what they can access to include what content they can receive. Agents with privileged access should operate in environments where their inputs can be validated and monitored.

​

The Gap Between Policy and Reality

​

What makes this situation particularly challenging is that the organisations most exposed are not the ones that have ignored PAM. They are frequently the organisations that have invested substantially in PAM programmes and believe their privileged access landscape is under control. Their vaults are populated. Their session recording is running. Their access reviews are happening quarterly.

But their PAM programme was scoped for a world where privileged access meant human administrators. The AI agents being deployed by their business units sit entirely outside that scope — using credentials that the PAM team has never seen, accessing systems that are not covered by session monitoring, and accumulating permissions that no access review process will flag.

​

Closing this gap does not require replacing a PAM programme. It requires extending it, deliberately and urgently, to cover the non-human identities that are now operating within the same privileged access space as human administrators.

​

That work is not simple. But the alternative leaving AI agents as unmanaged privileged actors within enterprise environments is a risk that no organisation can rationally accept.

iamethod specialises in PAM programme delivery and rescue. If your privileged access programme does not currently account for AI agent identities, we can help you understand the gap and close it.

Sources:

​

• IBM Cost of a Data Breach Report, 2024

• Verizon Data Breach Investigations Report, 2025

• Varonis, 2025 State of Data Security Report

• Microsoft, 2024 State of Multicloud Security Risk Report

• Obsidian Security, Security for AI Agents, 2024

• Okta Executive Survey, 2025 (n=260)

• Aembit, 6 Cybersecurity Risks of Agentic AI for Security Teams, January 2026