AI agents, MCP, and machine identities will reshape IAM in 2026

At a glance

What this is: Curity argues that 2026 will bring tighter focus on AI agents, MCP, machine identities, and runtime authorization as IAM adapts to autonomous systems.

Why it matters: IAM and NHI teams need to move beyond manual provisioning because autonomous applications make speed, trust, and dynamic permission evaluation core control problems.

👉 Read Curity's outlook on AI, MCP, and machine identity trends for 2026

Context

AI agents and MCP are changing identity from a human-centric control problem into a machine-centric one. Existing IAM models still assume slower onboarding, static trust, and review-heavy access decisions, but autonomous applications need dynamic authorization that can change as system signals change. For IAM and NHI practitioners, the question is no longer whether to support machine identities, but how to govern them without creating standing access paths that are too slow to remove.

Curity’s 2026 outlook ties that shift to practical pressure points: machine identities, OAuth integrations, token exchange, and runtime authorization. The underlying issue is not just authentication. It is whether organisations can define trust, scope permissions, and evaluate access continuously across systems that act faster than human approval workflows. That starting position is now typical for teams that run AI-enabled services, even if their governance model is still catching up.

Key questions

Q: How should security teams govern AI agents that use machine identities?

A: Treat each agent as a governed non-human identity with an owner, purpose, and scope boundary. Enforce least privilege at the tool and token level, then review access whenever the agent’s behavior, data sources, or downstream integrations change. If the agent can execute actions, it needs continuous authorization controls, not just initial onboarding.

Q: What is the difference between runtime authorization and traditional IAM reviews?

A: Traditional IAM reviews validate access before use, usually through periodic approval cycles. Runtime authorization evaluates a request at the moment it happens, using current context, policy, and risk signals. For AI agents and machine identities, runtime decisions are more suitable because permissions can change faster than human workflows can keep up.

Q: When does short-lived access still leave an organisation exposed?

A: Short-lived access still leaves exposure when the trust boundary is too broad. If a token can be minted, exchanged, or used with excessive scope, the attacker or misbehaving agent can still reach too much before expiration. Reduced lifetime lowers dwell time, but it does not fix bad delegation or overprivileged design.

Q: What is the difference between strong client authentication and least privilege?

A: Strong client authentication proves an application or workload is allowed to request a token. Least privilege limits what that token can do after it is issued. Both are necessary, but they solve different problems. Organisations often overfocus on authentication strength and underinvest in scope control, which is where most authorization failures appear.

Technical breakdown

Why runtime authorization matters for machine identities

Runtime authorization is the practice of deciding access at the moment a request is made, instead of relying only on a pre-approved entitlement set. That matters for machine identities because autonomous applications can change context quickly, and their permissions often need to follow system state, workload trust, or policy signals. Traditional IGA models are weak here because they assume slower reviews and human workflow. PAM helps with elevated access, but it is not designed to continuously arbitrate application-to-application requests. The operational problem is not simply who can log in, but what a workload can do right now, under current conditions.

Practical implication: Treat workload access as a continuously evaluated control problem, not a one-time entitlement assignment.

OAuth client credentials, token exchange, and trust boundaries

OAuth client credentials remain a common pattern for machine authentication, but they do not solve least privilege by themselves. In AI and MCP-connected environments, organisations increasingly need token exchange and JWT assertion flows to cross trust boundaries without reusing overly broad credentials. That allows a system to present a stronger identity, then obtain a narrower token for the specific downstream action. The architectural issue is that many implementations still treat the original client secret as the primary trust anchor, even when the real risk sits in the exchanged token’s scope and lifespan.

Practical implication: Audit where tokens are exchanged and narrowed, then remove any direct downstream privilege that bypasses scope reduction.

MCP changes the access-control model for AI tools

MCP introduces a structured way for AI agents to reach tools and data sources, but that also expands the identity perimeter. Once an agent can call tools on behalf of a user or itself, teams must distinguish between the agent’s own identity, the user’s delegated rights, and the permissions of each tool target. That creates a policy problem across multiple identities, not one identity. The security failure mode is overtrusting the agent as if it were a normal application, when in practice it can chain actions, cross boundaries, and amplify small permission mistakes into larger access paths.

Practical implication: Map every MCP-connected tool to a specific identity and scope, then enforce per-tool authorization rather than agent-wide trust.

Threat narrative

Attacker objective: The attacker wants to use an overprivileged machine or agent identity to move beyond intended scope and perform unauthorized actions across connected systems.

1. Entry occurs when an AI agent or workload uses client credentials or delegated access to reach tools and data sources through MCP-connected integrations.
2. Escalation happens when the agent is granted broader downstream permissions than required, often because token scope is not narrowed at exchange time.
3. Impact follows when the agent can chain tool actions across systems and access data or functions outside the intended trust boundary.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• Cisco DevHub NHI breach — IntelBroker exploited exposed Cisco credentials, API tokens and keys in DevHub.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

AI agents are now NHI governance problems, not just application features. Once an autonomous system can request tools, exchange tokens, and act across trust boundaries, it becomes a non-human identity with operational consequences. That means IAM teams must treat the agent lifecycle as part of identity governance, not as a separate AI concern. The practitioner conclusion is simple: if the agent can act, the agent must be governed like an identity.

Runtime authorization is becoming the decisive control plane. Static onboarding and periodic reviews do not match machine-speed access decisions. The field needs controls that evaluate context, policy, and downstream scope at request time, especially where permissions can change based on system signals. The practical conclusion is that slow governance will fail wherever the workload is autonomous.

Ephemeral credentials create a trust boundary problem, not a trust solution. Short-lived tokens reduce exposure windows, but they do not remove the need to define who or what is trusted to mint, exchange, and use them. That is the core of what we would call an identity blast radius: the smaller the scope, the smaller the damage when a workload or agent is misused. The practitioner conclusion is to shrink delegated scope before chasing shorter lifetimes.

OAuth maturity will be measured by how well it limits downstream authority. Strong client authentication is useful, but it is not enough when workloads need least privilege across multiple systems. Token exchange, assertion grants, and federated authorization only help if organisations actually constrain what each exchanged token can reach. The practitioner conclusion is to measure OAuth security by effective privilege, not by login strength alone.

Broken authorization will remain the most practical failure mode in API and agent environments. The article’s warning about implementation gaps is accurate because most incidents in this space come from mismatched assumptions between infrastructure, development, and security teams. That is exactly where NHI governance must become more explicit. The practitioner conclusion is to test authorization paths the same way attackers do, not the way architecture diagrams suggest they work.

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 44% of organisations have implemented policies to govern AI agents, even though 92% agree that governing them is critical to enterprise security, according to the same research.
• For a deeper control model, review OWASP Agentic Applications Top 10 and map the findings to your agent and NHI governance programme.

What this signals

With 96% of technology professionals already seeing AI agents as a growing security threat, the governance baseline is shifting faster than most IAM programmes can absorb. That pressure makes agent lifecycle ownership, scope design, and runtime checks the operational centre of NHI governance. The new control question is whether access can be made safe enough before autonomous systems normalise broad trust.

Identity blast radius: the practical risk is no longer the presence of an agent, but how far its delegated access can spread once it starts chaining tools. Teams should measure how much damage one compromised token, agent, or workload can cause across connected systems. In practice, that means shrinking the blast radius before expanding the automation footprint.

For practitioners

• Map every autonomous workload to an identity owner Assign a human owner, a business purpose, and a scope boundary for each AI agent, bot, and service account. Reassess the ownership record whenever the workload gains a new tool, data source, or downstream permission.
• Move high-risk access checks to runtime Use policy decisions at request time for sensitive actions, especially where access depends on context, request origin, or current workflow state. Keep manual approval only for exceptional paths that cannot be automated safely.
• Narrow OAuth tokens before they cross trust boundaries Prefer token exchange and assertion-based flows that reduce downstream privilege. Review every integration that still reuses broad client credentials across multiple systems or environments.
• Separate agent identity from user delegation Document when an AI agent acts as itself, when it acts on behalf of a user, and when it must be blocked from combining those roles. Enforce that distinction in policy, logging, and access reviews.
• Test for broken authorization paths continuously Run negative testing against API and MCP-connected workflows to confirm that identity checks fail closed. Include tool chaining, scope escalation, and cross-boundary calls in the test plan.

Key takeaways

• AI agents and MCP are pushing IAM into runtime authorization, where access decisions must be evaluated at the moment of use.
• Static onboarding and periodic review are no longer sufficient for autonomous workloads that can change context and chain actions quickly.
• Practitioners should govern agents as non-human identities, with scoped tokens, explicit ownership, and continuous authorization checks.

Key terms

• Runtime Authorization: Runtime authorization is the practice of deciding whether access is allowed at the moment a request is made. It uses current context, policy, and risk signals instead of relying only on static entitlement records, which makes it better suited to autonomous workloads and AI agents.
• Machine Identity: A machine identity is a credentialed identity used by software, workloads, APIs, bots, or AI agents rather than a human. It can include service accounts, tokens, certificates, or client credentials, and it must be governed with ownership, scope, and lifecycle controls.
• Identity Blast Radius: Identity blast radius is the amount of damage that can occur if an identity is abused, misconfigured, or compromised. It is shaped by privilege scope, token lifetime, trust boundaries, and how many systems the identity can reach before controls intervene.
• Token Exchange: Token exchange is an OAuth pattern that swaps one credential or token for another with narrower scope or different trust context. In NHI governance, it is useful when a workload must cross boundaries without carrying broad, reusable privileges into downstream systems.

Deepen your knowledge

AI agent identity governance and runtime authorization are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are adapting IAM for autonomous systems, it is worth exploring.

This post draws on content published by Curity: AI, MCP, and machine identity trends to watch in 2026. Read the original.