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.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “Introducing Intent Security: A Behavioral Baseline Framework for Agentic AI”.
Key questions
Q: What breaks when agentic AI is governed only with static policies?
A: Static policies assume the actor, context, and purpose stay stable long enough for review.
Q: Why do agentic AI systems need behavioural baselines instead of prompt filters?
A: Prompt filters only judge the current message, while behavioural baselines compare actions against expected patterns over time.
Q: What are the signs that an agent has drifted outside its intended scope?
A: Common signs include repeated attempts to reach restricted resources, unusual sequencing of tools, requests that expand beyond the original task, and behavior that bypasses normal review points.
Practitioner guidance
- Map agentic workflows to decision chains Identify where an agent carries context across turns, calls tools, or alters execution based on prior results.
- Define behavioural baselines for each agent Set expected patterns for goals, tool use, authority, and scope so deviations can be detected against a known profile rather than a generic policy rule.
- Place controls at execution time Use enforcement points that can block, mask, or escalate before an agent commits an action, especially where API calls or delegated actions can create irreversible impact.
Bottom line: Agentic AI changes the security problem from single-event inspection to cumulative behavioural governance across sessions and tool use.
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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
11/06/2026 1:34 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
12/06/2026 3:08 am
Stateless AI controls are already misaligned with agentic behaviour. The article correctly exposes a structural mismatch: controls built for isolated prompts cannot govern systems that remember context, chain actions, and adapt mid-execution. That is not a tuning issue, it is a category error. For practitioners, the lesson is that security models designed for single-turn AI do not survive multi-step autonomy.
A few things that frame the scale:
- A May 2025 Gartner poll of 147 CIOs and IT leaders found that 24% had already deployed AI agents, 50% were experimenting and 17% planned to deploy by the end of 2026.
A question worth separating out:
Q: How should teams decide where to enforce controls in agentic workflows?
A: Teams should enforce controls at the point where actions become real, such as tool invocation, API calls, or delegated execution, because that is where impact is created. If enforcement only happens after logging or review, the agent may already have crossed the line. The right test is whether the control can stop the action before it lands.
👉 Read our full editorial: Intent security redefines agentic AI governance beyond stateless controls