RSA 2026 vendor vision shows agentic AI security is now a category

At a glance

What this is: EMA’s 2026 Vendor Vision report spotlights ten security vendors, and its framing shows agentic AI security has moved into the mainstream of RSA 2026 discussion.

Why it matters: IAM and security teams need to understand how conference attention is shifting toward machine-speed threats, because that is where identity governance, NHI controls, and autonomous system oversight are converging.

By the numbers:

• With over 600 vendors and tens of thousands of cybersecurity professionals expected to attend, the report provides a curated roadmap to the most innovative solutions featured at the conference.
• 92% of organisations expose NHIs to third parties, raising concerns about supply chain security.
• 80% of identity breaches involved compromised non-human identities such as service accounts and API keys.

👉 Read Acalvio's coverage of EMA's 2026 Vendor Vision report

Context

RSA conference coverage often reads like a vendor parade, but the governance signal matters more than the product list. When a curated analyst report starts highlighting agentic AI security and autonomous enterprise defence, it indicates that identity teams are being pulled toward a new control problem: how to govern non-human actors that behave faster than traditional review and approval cycles.

For IAM, PAM, and NHI programmes, the practical question is not which booth looks most innovative. It is whether current identity controls can separate legitimate machine execution from uncontrolled privilege, especially as autonomous systems, service accounts, and AI agents increasingly share operational environments. The relevant challenge is governance depth, not conference marketing.

This is a typical inflection point for the category. Industry attention is moving from basic machine identity hygiene toward runtime trust, delegation control, and autonomous access governance, which means practitioner programmes will need to mature in step with the market.

Key questions

Q: How should security teams govern AI agents and machine identities together?

A: Security teams should govern them together only at the policy boundary, not by assuming they behave the same way. Service accounts are static non-human identities, while AI agents may change actions, tool use, and timing during execution. The practical goal is to separate inventory controls from runtime authority controls so each actor type is governed according to its actual behaviour.

Q: Why do machine-speed threats expose gaps in identity governance?

A: Machine-speed threats expose gaps because many identity programmes still depend on review cycles, manual certification, and delayed revocation. If access is created and used before a human can evaluate it, the control arrives after the risk has already moved. That makes runtime enforcement, not periodic oversight alone, the decisive governance layer.

Q: What do security teams get wrong about autonomous enterprise controls?

A: They often treat autonomy as a branding term rather than an identity behaviour change. The mistake is assuming that traditional service account controls are enough when a system can independently choose actions and timing. Governance has to start with the actor’s actual decision authority, otherwise the control model is misaligned from the outset.

Q: How can organisations tell whether their NHI programme is ready for agentic AI?

A: An NHI programme is ready only if it can answer who may initiate actions, what tools they may use, and when those permissions end. If those questions are still handled through static entitlement review alone, the programme is not ready for agentic behaviour. Readiness shows up in runtime policy, delegated scope, and fast containment.

Technical breakdown

Why agentic AI changes identity governance assumptions

Agentic AI alters identity governance because the actor can choose actions, select tools, and decide timing at runtime rather than only presenting a fixed credential. That makes traditional provisioning logic weaker, because the subject being authorised is no longer a static workload with predictable behaviour. The identity signal now includes intent, delegation, and session-level drift, which is why generic machine identity patterns do not fully cover agentic systems. For IAM teams, this pushes the problem from account management into runtime authorisation and containment.

Practical implication: teams need controls that observe behaviour during execution, not just at onboarding.

Machine-speed threats and the limits of review cycles

Machine-speed threats compress the time between access, use, and impact. In conventional NHI governance, review processes assume there is enough stable state to inventory, certify, and revoke. With autonomous or heavily automated systems, that assumption weakens because the access path can be created, used, and compounded before a human review cycle begins. This is why governance has to include live policy enforcement, narrow delegation, and event-driven containment rather than only periodic certification.

Practical implication: review cadences must be supplemented with runtime policy and automated containment triggers.

What a secure autonomous enterprise needs from identity controls

A secure autonomous enterprise needs identity controls that can handle ephemeral access, scoped delegation, and clear accountability across human, NHI, and agentic actors. The architectural issue is not simply more authentication. It is defining which actor is allowed to invoke tools, under what conditions, and how that privilege is terminated when the task ends or the system changes state. Without that structure, machine identities become a fast path to broad operational exposure.

Practical implication: define delegated authority boundaries before systems are allowed to operate independently.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• AI LLM hijack breach — attackers used stolen AWS access keys to hijack Anthropic LLM models on Bedrock.

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

NHI Mgmt Group analysis

Agentic AI security is becoming an identity governance problem, not just a detection problem. EMA’s framing shows the market is moving from narrow tool security toward the harder question of who or what is permitted to act. That shift matters because the control surface now includes runtime decisions, delegated actions, and machine-speed execution paths. For identity teams, the field is converging on governance of behaviour, not just credentials.

Identity programmes built only for human review cycles will not keep pace with autonomous enterprise operations. The report’s emphasis on machine-speed threats reflects a broader failure of cadence-based governance when access can be used before a certifier even sees it. This does not mean every automated system is autonomous, but it does mean the old assumption that access persists long enough to be reviewed is under pressure. Practitioners should treat that as a structural change in control design.

Runtime delegation will become the central trust question for NHI and agentic systems. As more vendors target AI agents and autonomous enterprise use cases, the market is signalling that static entitlements are no longer enough. The hard question is which identities may initiate tool use, combine permissions, and act without a human gate in the loop. That is where governance, PAM, and NHI control models will need to meet.

RSA attention is a signal that the category is moving from visibility to enforcement. Conference curation increasingly rewards solutions that address how access is exercised, not just how it is discovered. That transition is important for practitioners because it suggests the next wave of NHI maturity will be measured by policy enforcement, delegation boundaries, and containment rather than asset counts alone. The programme implication is clear: inventory is necessary, but it is no longer sufficient.

From our research:

• 80% of identity breaches involved compromised non-human identities such as service accounts and API keys, according to the Ultimate Guide to NHIs.
• From our research: 71% of NHIs are not rotated within recommended time frames, which leaves long-lived machine access exposed to persistence risk.
• Forward pivot: The NHI Lifecycle Management Guide shows how provisioning, rotation, and offboarding controls reduce that exposure across the full identity lifecycle.

What this signals

Runtime delegation will become the differentiator in NHI programmes. Conference attention is moving toward autonomous enterprise security, which means practitioners should expect greater scrutiny of who can initiate machine actions and under what conditions. The organisations that separate inventory from authority will have a cleaner path to enforcing NHI governance across service accounts, API keys, and agents.

93% of organisations are already exposed to some form of NHI-related security gap if they cannot fully see or rotate their machine identities, and that gap widens when autonomy enters the picture. The practical signal is that agentic AI will not replace NHI governance, it will stress-test it. Teams should prepare for controls that tie runtime enforcement to identity lifecycle management instead of relying on periodic review alone.

Agentic AI expands the trust boundary beyond the credential. When a system can select tools and sequence actions independently, the control question shifts to delegation, containment, and accountability. That is why practitioners should align roadmaps with standards such as the NIST Cybersecurity Framework 2.0 and the Anthropic report on AI-orchestrated cyber espionage where runtime behaviour is the real risk surface.

For practitioners

• Map autonomous decision points separately from NHI inventory Identify where systems can choose actions, tools, or timing without a human approval gate. Treat those execution points as a distinct governance class rather than folding them into ordinary service account review.
• Harden runtime delegation boundaries Define which identities may invoke tools, combine privileges, and hand off actions to other systems. Restrict those pathways to the smallest viable scope and document the termination condition for each delegation path.
• Shorten the gap between detection and containment Pair identity monitoring with automatic containment rules so risky behaviour can be interrupted before it compounds. For autonomous or fast-moving machine identities, periodic review alone is too slow to be protective.
• Use conference signal as a roadmap for programme prioritisation Treat the market attention around agentic AI and autonomous enterprise security as a clue to where governance gaps will surface next. Align roadmap work to runtime policy, delegated authority, and review automation rather than only expanding inventories.

Key takeaways

• EMA’s RSA 2026 vendor vision is a market signal that agentic AI security is moving into mainstream identity governance discussions.
• Machine-speed threats challenge review-based IAM models because autonomous or fast-moving actors can create and use access before certification catches up.
• Practitioners should prioritise runtime delegation controls, containment, and lifecycle governance so NHI and agentic systems are managed as behaviour, not just as credentials.

Key terms

• Agentic AI: An AI system that can decide what to do, which tools to use, and when to act during runtime. In identity governance, the key issue is not intelligence alone but whether the system can initiate access-bearing actions without a human approval gate.
• Runtime delegation: The act of granting or exercising authority during execution rather than only at provisioning time. For autonomous and agentic systems, this is the point where identity policy becomes operational, because the system may chain actions, invoke tools, and extend scope within a live session.
• Autonomous enterprise: An operating model in which software agents and machine identities perform meaningful work with limited human intervention. The governance challenge is that access, accountability, and containment must work at machine speed, not just through periodic review or after-the-fact certification.
• Machine-speed threat: A threat that progresses faster than manual identity controls can reasonably observe or stop. In practice, it turns short-lived access misuse into a governance problem because the window for detection, decision, and revocation may close before the control cycle completes.

Deepen your knowledge

Agentic AI security and runtime delegation are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are extending identity governance from service accounts into autonomous systems, it is worth exploring.

This post draws on content published by Acalvio: EMA's 2026 Vendor Vision report ahead of RSAC 2026. Read the original.