Autonomous AI is entering production environments where agents can access systems independently, make decisions, and execute tasks across complex enterprise landscapes. This reveals the limitations of traditional IAM models, which were designed for static users and predictable machine identities. As AI becomes more dynamic and context-driven, organizations need a new approach to identity governance that can keep up with real-time behavior.
In this article, we explore Agentic IAM, which extends Zero Trust through continuous authorization, intent-aware access control, and contextual risk evaluation. This ensures that every action is verified and policy-aligned at execution time.
Meanwhile, industries such as energy, utilities, and finance are facing increasing regulatory pressure from frameworks like NIS2 and KRITIS, making robust AI governance mandatory.
Organizations are expanding their management of identities beyond human and service to include AI agents, copilots, and autonomous decision systems. Each of these actors requires clear identity, accountability, and governance. This is particularly apparent in regulated and critical sectors such as energy, utilities, healthcare, finance, and industry. In these sectors, AI increases efficiency but also expands the attack surface and operational risk.
In response, cybersecurity is shifting toward continuous trust verification, real-time risk assessment, and dynamic policy enforcement. Traditional, perimeter-based identity and access management is evolving into adaptive models aligned with Zero Trust principles and extended to autonomous systems through agentic IAM.
Meanwhile, regulations such as NIS2 and KRITIS are raising the bar for transparency, traceability, and resilience in digital operations. Consequently, Agentic IAM is emerging as a fundamental control layer for the secure and compliant adoption of AI.
The zero-trust principle of "never trust, always verify" is stricter for AI agents because they can change their actions during a process. Decisions must be made per tool invocation or resource call, not just at the start of a session.
Behavior drift: The agent is accessing new applications or data types it has never touched before.
Data sensitivity: Accessing personally identifiable information (PII), protected health information (PHI), or payment card industry (PCI) data versus public knowledge bases.
Contextual factors: Time of execution, user location indicators, workload context, and running from unapproved regions.
Risk posture changes: Owner's account is compromised, model version is changed, or policy is updated.
Automatically downgrade privileges when unusual access patterns appear.
Require real-time human approval for elevated actions.
Sandbox the action for review.
Stop execution and trigger incident workflows.
Security architects should treat the continuous policy engine for Agentic IAM similarly to CASB or CSPM: always on and feeding from multiple telemetry sources, integrated with threat detection capabilities. This zero-trust architecture enables security and operational efficiency by allowing agents to operate autonomously within verified boundaries while detecting anomalies that indicate suspicious behaviour.
In highly regulated environments, particularly in Germany, Austria, and Switzerland, identity and access management must align with strict cybersecurity frameworks, such as the NIS2 Directive and KRITIS. These frameworks impose strict requirements on organizations classified as operators of essential or critical services, including energy providers, utilities, healthcare institutions, and financial systems.
KRITIS (Kritische Infrastrukturen) refers to organizations whose failure or disruption would result in significant supply shortages or threats to public safety. According to German regulations, these operators must implement cutting-edge cybersecurity measures.
Similarly, the NIS2 Directive expands the scope and enforcement of cybersecurity obligations across the EU, mandating stricter accountability, real-time risk management, and improved incident reporting timelines.
Traditional IAM models are inadequate for meeting these regulatory expectations when applied to autonomous AI agents. Static access controls, periodic reviews, and coarse logging mechanisms do not provide the traceability, responsiveness, and control required by NIS2 and KRITIS. Instead, agentic IAM becomes a regulatory enabler, offering the following:
Continuous authorization and monitoring aligned with real-time risk management obligations,
Fine-grained, context-aware access control to ensure least-privilege enforcement,
Full decision traceability to support auditability and regulatory reporting,
Dynamic identity lifecycle governance to reduce the risk of unmanaged or noncompliant agents.
KRITIS regulations were originally designed for human-operated, deterministic systems. However, the rapid integration of AI into critical infrastructure is prompting a change. Autonomous systems now play a role in grid balancing, predictive maintenance, outage response, and automated trading in energy markets.
These systems create a new category of "critical identities" - AI agents that act independently within sensitive operational environments. According to KRITIS, every action that could affect service continuity must be attributable, controllable, and reversible.
Operators of KRITIS systems must comply with strict cybersecurity and resilience regulations. These regulations include:
Meanwhile, energy and utility companies are quickly implementing AI-driven systems in grid operations, predictive maintenance, load balancing, renewable energy integration, compliance processes, and field force automation. Nearly 40% of utility control rooms are expected to adopt AI-driven operators by 2027. These operators will be capable of providing insights and taking autonomous actions across critical environments.
Utilities must now manage thousands of non-human identities and autonomous AI agents that interact across OT, IT, cloud, and SaaS environments. They must also comply with increasingly complex regulatory requirements that demand transparency, accountability, and traceability. Traditional IAM models based on static service accounts and role-based access control are insufficient for this level of operational autonomy.
"In energy and electricity, Agentic IAM is intent-aware, just-in-time access control for AI agents and machine identities operating critical infrastructure autonomously - ensuring full auditability, regulatory compliance, and built-in security guardrails across the entire energy ecosystem".
Bartosz Radecki, Director of Product Management at Unravel
As autonomous AI systems take on more decision-making and operational responsibility across enterprise environments, Agentic IAM is becoming essential. It introduces continuous authorization, intent-aware controls, and real-time risk evaluation to enable secure and compliant AI autonomy. This is especially critical in regulated industries, where accountability, traceability, and resilience are mandatory. As artificial intelligence adoption accelerates, Agentic IAM will serve as a foundational layer for safely scaling autonomous systems.
At Unravel, we offer a control and orchestration layer that governs how AI agents interact with critical infrastructures using existing IAM, PAM, and enterprise security tooling.
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