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06/09/2026 9:34 am
TL;DR: RSAC 2026 sessions from Cisco, CrowdStrike, Microsoft, and Okta converged on a clear finding: AI agents are already inside enterprises, but most security teams still cannot observe, govern, or baseline them, according to Vorlon. The unresolved problem is not adoption, but action-level identity control and behavioural detection for autonomous systems.
NHIMG editorial — based on content published by Vorlon: agentic security themes from RSAC 2026
By the numbers:
- Enterprise adoption of AI agents went from 1% of organisations to 91% between Q1 2024 and late 2025.
- The MCP server registry grew from 100 servers to over 6,000 in roughly twelve months.
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes, and as quickly as 9 minutes in some cases.
Questions worth separating out
Q: What breaks when AI agents are discovered too late or not at all?
A: When agents are not discovered early, teams cannot assign ownership, set scope, or prove what systems the agent can reach.
Q: Why do AI agents need action-level authorisation instead of resource-level access?
A: Resource-level access tells you what an agent can reach, but not whether its runtime actions match its authorised purpose.
Q: How do security teams know if AI governance is working?
A: Look for evidence that access decisions are reviewable, permissions are revocable, and exceptions are not becoming permanent.
Practitioner guidance
- Map every deployed agent before writing policy Build a discovery inventory that includes enterprise-sanctioned agents, shadow AI, low-code deployments, and SaaS-bundled assistants.
- Assign identity and ownership to each agent Create a named owner, scope, and lifecycle record for every agent just as you would for a privileged service account.
- Shift authorization from resources to actions Define which actions an agent may take, in which contexts, and for which business purpose.
What's in the full article
Vorlon's full article covers the operational detail this post intentionally leaves for the source:
- Direct session-by-session notes from Cisco, CrowdStrike, Microsoft, and Okta with the specific framing each speaker used
- The exact examples behind agent discovery, action-level authorization, and MCP supply chain risk
- The session footnotes and transcript references that support the article’s interpretation
- The conference context around how enterprise teams are currently talking about AI agent governance
👉 Read Vorlon’s analysis of agentic security themes at RSAC 2026 →
AI agent identity at RSAC 2026: what changed for security teams?
Explore further
06/09/2026 9:34 am
Discovery is now the control plane for agent governance: the field cannot govern what it has not enumerated. That means inventory is no longer a documentation exercise, but the prerequisite for identity, policy, and response. In practice, discovery has to cover sanctioned, shadow, and embedded agents across all business units.
A few things that frame the scale:
- Organisations maintain an average of 6 distinct secrets manager instances, creating fragmentation that undermines centralised control, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, according to The State of Secrets in AppSec.
A question worth separating out:
Q: What is the difference between human access review and AI agent access review?
A: Human access review focuses on stable job roles and periodic entitlement checks. AI agent access review must also account for runtime behaviour, changing integrations, token lifetimes, and delegated actions across SaaS systems. Agents can change what they touch faster than a standard access review cycle expects.
👉 Read our full editorial: Agentic security at RSAC 2026 exposes the identity gap
