Security teams will wrestle with agentic AI and NHI in 2026

At a glance

What this is: A short Keyfactor newsroom item forecasting that security teams and MSSPs will grapple with agentic AI and non-human identities in 2026.

Why it matters: It matters because IAM, PAM, and NHI programmes now have to govern machine identities and autonomous actors together, not as separate edge cases.

👉 Read Keyfactor's newsroom note on agentic AI and non-human identities in 2026

Context

Agentic AI and non-human identities are now converging into the same governance problem: who or what is allowed to act, with which privileges, and under whose accountability. Traditional IAM assumes stable subjects and reviewable access patterns, but AI-driven execution and machine credentials introduce faster-changing trust boundaries.

Keyfactor’s framing points to an industry reality security teams are already confronting. The challenge is no longer just secret rotation or certificate hygiene. It is the need to govern runtime identity behaviour across workload access, delegated tools, and autonomous actions without losing auditability or control.

Key questions

Q: How should security teams govern AI agents that use non-human identities?

A: Security teams should govern AI agents as runtime actors that sit on top of non-human identities, not as ordinary applications. That means defining tool scope, limiting credential privilege, and attaching ownership and telemetry to every action path. If the agent can expand what it can do during execution, the control model has to track behaviour, not just authentication.

Q: Why do non-human identities become more risky when agentic AI is involved?

A: Non-human identities become more risky because agentic systems can combine credentials, tools, and timing decisions in ways static provisioning did not anticipate. A service account that is merely over-permissioned is already risky. An agent using that account can turn the same weakness into rapid, chained action across multiple systems, which increases blast radius and complicates review.

Q: What do security teams get wrong about AI agent governance?

A: Teams often treat agent governance as a policy wrapper around a model or application. That misses the real issue, which is identity behaviour at runtime. If the agent can choose actions, invoke tools, and complete work without human intervention, then governance must cover those execution paths directly rather than assuming ordinary IAM reviews will catch them later.

Q: How can organisations prove accountability for agentic and machine actions?

A: Organisations prove accountability by capturing end-to-end identity telemetry that ties each action to a credential, an initiating identity, and a business owner. They also need revocation evidence and scope logs so investigators can see whether access stayed inside its intended boundary. Without that chain, audit and incident response both become guesswork.

Technical breakdown

Why agentic AI changes identity governance

Agentic AI matters to identity governance because the actor does not merely authenticate and request access. It can decide which tool to use, when to use it, and what sequence of actions to take. That creates a different control problem from ordinary workload identity or scripted automation. In practice, the issue is not just credential issuance. It is whether the identity can expand its own effective reach during execution in ways that static provisioning never anticipated. That is why agentic behaviour has to be treated as an identity design problem, not just an application feature.

Practical implication: map agent behaviour to explicit identity boundaries before allowing tool access or downstream delegation.

Non-human identities are the baseline control plane

Non-human identities remain the structural baseline because every agent, service, API key, token, or certificate still depends on some form of credentialed access. The difference is that agentic systems can combine those credentials with runtime choices that make the blast radius harder to predict. Governance therefore has to cover provisioning, secret scope, lifecycle, and revocation in a single model rather than as isolated controls. If the underlying NHI is weak, the agent inherits that weakness immediately. If the NHI is over-permissioned, the agent can amplify it at machine speed.

Practical implication: inventory and constrain every machine credential before layering agentic behaviour on top.

Auditability becomes the deciding control

Auditability is where many identity programmes will feel the pressure first. Security teams can only govern what they can observe, and autonomous or semi-autonomous systems often create action chains that are faster and more distributed than standard review processes expect. For MSSPs, this changes the service model as well. They need evidence of who initiated access, which credentials were used, what tools were touched, and whether the resulting action stayed inside approved boundaries. Without that traceability, incident response and compliance both degrade quickly.

Practical implication: require end-to-end identity telemetry for machine and agent actions before relying on them operationally.

NHI Mgmt Group analysis

Agentic AI turns identity governance from a provisioning problem into a runtime control problem. The central shift is that access is no longer only granted at setup and reviewed later. An autonomous actor can choose tools, chain actions, and change its effective privilege footprint during execution. That breaks the assumption that identity states are stable enough to certify after the fact. The practitioner conclusion is simple: review-based IAM alone cannot describe or govern agent behaviour.

Non-human identity remains the control substrate even when the subject is an AI agent. Agentic systems still depend on certificates, tokens, keys, and delegated access to do useful work. That means the quality of NHI governance determines the security ceiling for agentic deployments. Weak lifecycle hygiene, overbroad scopes, and opaque credential sprawl will surface as agent risk, even when the AI logic itself is sound. The practitioner conclusion is that NHI discipline is now a prerequisite for safe agent adoption.

Runtime accountability is the named concept security teams need to operationalise. The issue is not just whether access exists, but whether teams can prove which identity acted, what it touched, and when the action crossed an approval boundary. In an agentic environment, accountability is only as strong as the identity telemetry attached to every step. The practitioner conclusion is that auditability must be designed as a control objective, not treated as a logging afterthought.

MSSPs will need to repackage identity services around autonomous behaviour, not only credential hygiene. The market signal is that buyers will ask for evidence of agent governance, machine identity oversight, and runtime traceability in the same conversation. That raises the bar for service design, reporting, and incident handling. The practitioner conclusion is that managed identity services will be judged on behavioural visibility as much as on secret management.

The governance gap is now cross-domain, spanning human IAM, NHI, and agentic AI. Policies written for human approval flows do not map cleanly to machine-speed decisions, but NHI controls alone are also insufficient when an agent can act on top of them. That forces identity teams to think in terms of delegated authority, observable execution, and lifecycle ownership across all actor types. The practitioner conclusion is to align governance by behaviour, not by identity label.

From our research:

• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
• Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
• That visibility gap points directly to the next control priority, so readers should also review OWASP Agentic AI Top 10 for the runtime risk patterns shaping agent governance.

What this signals

Runtime accountability: identity programmes should assume that review evidence will be generated by the system itself, not by a later manual audit. That shifts design priority toward action-level telemetry, ownership mapping, and revocation proof, especially where AI agents and workload identities share the same execution path.

With 96% of technology professionals identifying AI agents as a growing security threat, the programme signal is clear: agent governance is becoming a mainstream identity requirement, not a niche AI project. Teams should align policy, logging, and approval boundaries before more deployments move into production.

For teams building out governance coverage, the NHI Lifecycle Management Guide remains the right lens for provisioning, rotation, and offboarding discipline across machine identities, while the NIST AI Risk Management Framework helps anchor ownership and oversight for autonomous behaviour.

For practitioners

• Define agent-specific identity boundaries Document which tools, data sets, and downstream identities each agent may access, then bind those permissions to a named business owner and a defined runtime purpose.
• Tighten machine credential lifecycle controls Apply short-lived credentials, explicit revocation paths, and scope limits to every service account, token, and certificate an agent can use.
• Instrument end-to-end identity telemetry Log the initiating identity, credential used, tool invoked, and resulting action so that security and compliance teams can reconstruct behaviour after the fact.
• Separate human approvals from machine execution Require clear approval boundaries for high-risk actions and do not allow agents to inherit broad standing access simply because a human approved the workflow.
• Reassess MSSP reporting for autonomous behaviour Update service reporting to include agent scope drift, privileged action chains, and revocation evidence, not just credential inventory and rotation status.

Key takeaways

• Agentic AI changes identity governance because runtime behaviour can expand privilege in ways provisioning-time policy cannot fully predict.
• Non-human identity controls remain the foundation, but they now have to support autonomous execution, auditability, and tighter blast-radius management.
• Security teams should treat agent identity telemetry and lifecycle discipline as prerequisite controls, not optional enhancements.

Key terms

• Agentic AI identity: The identity posture of software that can decide and execute actions at runtime, rather than only following a fixed script. In practice, it combines authentication, delegated access, and behavioural control so that tool use, timing, and scope can be governed as one identity problem.
• Non-human identity: A machine or software identity used by services, workloads, bots, scripts, APIs, certificates, or tokens. It exists to let systems authenticate and act without a person present, which means lifecycle, privilege, and revocation controls must be applied with the same discipline as human access.
• Runtime accountability: The ability to tie each machine or agent action to an initiating identity, credential, owner, and approval path at the moment it occurs. It is stronger than simple logging because it supports investigation, compliance, and containment when behaviour changes faster than periodic review cycles.
• Scope drift: When an identity, especially an agent or workload identity, begins operating outside the access or purpose originally assigned to it. The risk is not limited to over-permissioning at setup, because runtime behaviour can widen effective access through chained tools, delegation, or repeated execution.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.

This post draws on content published by Keyfactor: Security Teams, MSSPs Will Wrestle with Agentic AI, Non-Human Identities in 2026. Read the original.