Redefining PAM for a Workforce of Humans and AI Agents

This article explores how CISOs, CTOs and IAM leaders can redesign their PAM and identity architectures to safely govern the next-generation workforce.

AI agents are becoming active participants in enterprise operations. They initiate workflows, access sensitive data, and increasingly perform tasks traditionally handled by skilled human administrators. Yet in many organisations, these agents operate without clear identity, privilege boundaries or monitoring.

This is no longer sustainable.

As automation accelerates, Privileged Access Management (PAM) must evolve from a human-centric control to a unified framework governing both human and AI-powered identities. PAM is quickly becoming the decisive safeguard against privilege misuse, runaway automation and opaque decision-making.

This article explores how CISOs, CTOs and IAM leaders can redesign their PAM and identity architectures to safely govern the next-generation workforce.

 

The AI Workforce Is Expanding – and So Is Its Privilege Surface

AI agents now exist across IT and business functions: embedded in SaaS applications, orchestrating cloud workflows, responding to customer queries, and analysing operational telemetry. Platforms such as AWS Bedrock AgentCore accelerate this adoption by making AI-driven workflows easy to deploy at scale.

However, privilege management for AI agents is often immature.

Typical issues include:

  • Agents using shared or hard-coded credentials
  • Standing administrative privileges embedded in automation pipelines
  • No session monitoring or behavioural tracking
  • Unclear ownership for non-human identities

This means there is a hidden privileged workforce operating with inconsistent governance and limited oversight.

 

Why PAM Must Expand Beyond Human Administrators

Delinea’s recent research highlights a shift: as AI agents gain operational autonomy, they often hold more reliable access to systems and data than human administrators. Their actions are faster, less predictable and potentially more impactful.

This places new expectations on PAM.

AI agents require the same controls as human privileged users – and in some areas, stricter ones.

 

PAM must ensure:

  • Agents only receive the privileges required for specific tasks
  • Privileges are granted dynamically, not permanently
  • All sessions – human or AI – are monitored and auditable
  • AI behaviour is continuously validated for anomalies

Without these controls, AI-driven operations can unintentionally escalate risk.

 

Designing a Unified Privilege Model for Humans and AI Agents

 

First-class identities for AI agents

Every agent must have:

  • A unique identity
  • A defined owner
  • A transparent purpose and scope

IAM and CIAM platforms (e.g., Okta/Auth0) help eliminate shared credentials and enforce authentication standards.

 

Privilege segmentation and policy boundaries

Least privilege must apply consistently:

  1. Break down large automation roles into granular entitlements
  2. Ensure strict separation between development, test and production access
  3. Apply context-aware access rules based on task type and sensitivity

 

Just-in-time access for automation

Long-lived privileges are a major source of risk.

PAM should issue ephemeral credentials to AI agents, allowing them to perform specific actions and then immediately revoking access.

 

Session monitoring and anomaly detection

AI agents don’t always behave deterministically.

PAM must:

  • Log all actions
  • Monitor sessions in real time
  • Detect behavioural drift or policy deviations

This is critical for preventing privilege misuse at machine speed.

 

Observability as the Runtime Control Plane

Even the strongest IAM and PAM controls require validation. Dynatrace’s State of Observability 2025 highlights that while enterprises are deploying AI widely, they still lack confidence in automated decision-making. The gap is runtime oversight.

 

Telemetry linked to identity and privilege

Observability must correlate:

  1. Who performed an action (human or agent)
  2. What privilege was used
  3. What system or data was accessed

This provides the context needed to trust automated operations.

 

Detecting drift in agent behaviour

AI agents adapt and learn.

Observability exposes when tasks or behaviours diverge from expected patterns – allowing CISOs and IT teams to intervene early.

 

Compliance evidence for automated workflows

European frameworks such as NIS2, DORA and the forthcoming Digital Omnibus place strong emphasis on:

  • traceability,
  • accountability,
  • and verifiable controls.

Observability supplies the audit trail required to demonstrate that AI-initiated privileged actions are governed and reviewable.

 

Cloud-Native PAM for AI Agents in AWS

At AWS re:Invent 2025, new integrations between AWS services and Dynatrace highlighted how organisations can secure AI agents at cloud scale.

For AI workloads on AWS, Cloudcomputing recommends:

  • Using AWS IAM roles for machine identities, bound to strict privilege policies
  • Integrating PAM (Delinea) for session monitoring and JIT access
  • Applying observability for runtime verification
  • Feeding identity and privilege events into AWS Security Hub and GuardDuty for threat detection

This layered approach ensures AI agents cannot act outside policy without detection.

 

Regulatory Pressure: PAM as a Compliance Imperative

Regulators increasingly expect organisations to demonstrate:

  • Strong identity governance for human and non-human users
  • Defined ownership for automated processes
  • Validated access decisions
  • Complete audit trails for privileged operations

Under frameworks such as NIS2, DORA and the Digital Omnibus, PAM becomes a central control for ensuring safe automation, especially where AI agents interact with sensitive systems or financial and operational processes.

 

The Target Operating Model: PAM at the Core of a Hybrid Workforce

A mature model for the future workforce incorporates:

Identity-first design

  • Both humans and AI agents are onboarded, governed and reviewed through consistent processes.

Privileged access by design

  • Standing privileges are eliminated.
  • All privileged actions require JIT authorisation and continuous validation.

Observability-driven assurance

  • Runtime telemetry confirms that privileged actions – especially those initiated by AI – remain aligned to policy and business intent.

A unified control architecture

  • IAM/CIAM → identity foundation
  • SailPoint → lifecycle governance
  • Delinea → privileged access control
  • Observability → verification and detection
  • AWS/Azure/GCP → cloud-native enforcement

 

How Cloudcomputing Helps Organisations Modernise PAM for AI

Cloudcomputing supports CISO and CTO teams with:

PAM strategy and roadmap for hybrid workforces

  • Defining a privilege model that equally governs human and AI identities.

Delinea-based PAM deployments

  • Implementing vaulting, JIT access, session monitoring and AI-ready automation controls.

Integrating IAM with PAM

  • Using Okta/Auth0 and SailPoint to create a coherent identity fabric across human and non-human actors.

Dynatrace observability for privileged actions

  • Delivering runtime validation, anomaly detection and end-to-end auditability.

Regulatory mapping for NIS2, DORA and Digital Omnibus

  • Aligning identity, privilege and monitoring controls with European supervisory expectations.

 

Where This Leaves CISOs and CTOs

AI agents are now part of the enterprise workforce, and they bring significant privilege implications. PAM can no longer be limited to human administrators – it must govern all identities, human or machine, with equal rigour.

Organisations that modernise their PAM operating model today will:

  • reduce risk from uncontrolled automation,
  • strengthen compliance posture,
  • and accelerate safe AI adoption.

The organisations that master PAM for AI agents now will set the benchmark for secure, automated operations in the decade ahead.