Notifications
Clear all
Topic starter
09/06/2026 11:59 pm
TL;DR: Agentic AI introduces memory, tool use, and execution paths that make stateless prompt inspection insufficient, while Lasso Security says its Intent Security framework evaluates behaviour across sequences of decisions, with risky actions blocked in under 50 milliseconds and detection accuracy improved by up to 80 percent. The core issue is that existing controls assume isolated interactions, but agentic systems create cumulative behavioural risk that must be governed over time.
NHIMG editorial — based on content published by Lasso Security: Introducing Intent Security, a behavioral baseline framework for agentic AI
By the numbers:
- Risky actions can be blocked in under 50 milliseconds, with broader session-level behavioral analysis completing in under five seconds.
- Detection accuracy across AI threat categories improves by up to 80 percent compared to stateless inspection models.
- The multi-model evaluation engine operates up to 570 times faster than conventional sequential approaches, enabling continuous behavioral monitoring across users, agents, and tool chains.
Questions worth separating out
Q: How should security teams govern agentic AI that can change behaviour mid-session?
A: Security teams should govern agentic AI with controls that evaluate the full decision chain, not only isolated prompts or outputs.
Q: Why do stateless AI controls fail for agentic systems?
A: Stateless controls fail because agentic systems carry memory forward, chain tool calls, and adjust plans across multiple steps.
Q: How can organisations tell whether an AI agent is drifting out of scope?
A: Look for divergence between the agent's observed actions and its historical baseline for mission, authority, and typical tool use.
Practitioner guidance
- Map agentic workflows to decision chains Document where each agent retains memory, selects tools, and changes plan across a session.
- Separate intent checks from baseline checks Validate the current request against the stated goal first, then compare the resulting behaviour against historical patterns for the same user, agent, or application.
- Attach enforcement to the execution path Design blocking, masking, and escalation so they can intervene before a harmful action completes, rather than waiting for downstream review or alert triage.
What's in the full article
Lasso Security's full research covers the operational detail this post intentionally leaves for the source:
- The step-by-step stateful evaluation flow for monitoring users, agents, tools, and applications across a live session.
- The enforcement logic behind real-time blocking, data masking, and escalation when behavioural thresholds are exceeded.
- The performance and accuracy claims that sit behind the framework, including speed, false positives, and multi-model evaluation.
- The implementation view for complex multi-agent environments that need continuous monitoring without disrupting user experience.
👉 Read Lasso Security's analysis of intent security for agentic AI environments →
Agentic AI behavioral baselining: what IAM teams need to know?
Explore further
10/06/2026 2:34 am
Stateless security is the wrong baseline for agentic AI. The control model that inspects prompts and outputs independently was designed for linear exchanges, not systems that remember, plan, and execute across multiple steps. Once the agent can carry context forward, risk accumulates across the full sequence rather than appearing at a single checkpoint. Practitioners should treat behavioural trajectory as the real security object, not the individual interaction.
A few things that frame the scale:
- 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, according to AI Agents: The New Attack Surface report.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
A question worth separating out:
Q: What should teams do when agentic AI needs real-time enforcement?
A: Teams should build blocking, masking, and escalation into the execution path so risky behaviour is interrupted before operational impact occurs. That requires stateful monitoring across users, agents, tools, and applications, plus thresholds that are tuned to act at machine speed rather than after the session has ended.
👉 Read our full editorial: Intent security redefines agentic AI governance beyond stateless controls
