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10/06/2026 12:21 am
TL;DR: AI agent risk develops across configuration changes, runtime behavior, and multi-step interactions, but most security tools still rely on periodic scans and stateless analysis, according to Zenity. That leaves teams blind to attacks that unfold over time, and it makes continuous context the new governance baseline for agent security.
NHIMG editorial — based on content published by Zenity: The Shift to Continuous Context and the Rise of Guardian Agents
Questions worth separating out
Q: How should security teams govern AI agents that change configuration and behaviour during runtime?
A: Security teams should govern AI agents as stateful identities, not static assets.
Q: Why do snapshot scans fail to manage AI agent risk effectively?
A: Snapshot scans fail because they capture a moment that may already be obsolete when the next interaction begins.
Q: What signals indicate that an AI agent has moved outside its intended risk boundary?
A: Look for permission drift, unexpected connector additions, chained tool use across multiple turns, and runtime actions that line up with posture weaknesses.
Practitioner guidance
- Correlate posture and runtime telemetry Build a single view that links agent configuration, permissions, and live actions so a drift event can be judged in context instead of as an isolated alert.
- Treat connector changes as governance events Register new tools, data sources, and permissions as immediate security events, then force review of the resulting exposure state before the next autonomous task runs.
- Adopt session-level detection rules Write detections that inspect a full interaction chain, including memory updates and multi-turn tool use, rather than relying on one-prompt policy checks.
What's in the full article
Zenity's full blog post covers the operational detail this post intentionally leaves for the source:
- A closer look at the stateful threat engine and how it evaluates interaction chains across sessions.
- The event-driven ingestion model used to refresh exposure state as agent configurations change.
- How contextual risk objects combine posture, permissions, and runtime activity into one issue.
- Examples of the specific runtime detections Zenity associates with multi-step prompt injection and tool misuse.
👉 Read Zenity's analysis of continuous context and Guardian Agents for AI security →
AI agent runtime risk is continuous, not snapshot-based?
Explore further
11/06/2026 2:06 am
Continuous context is the right control model for AI agents because risk now develops across state, not moments. Periodic scans assume the environment is stable long enough to observe, classify, and act. Zenity’s framing shows that AI agents mutate through configuration changes, memory updates, and chained interactions, so the control plane must track the sequence rather than the snapshot. Practitioners should understand that stateful AI behaviour cannot be governed like a static workload.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- 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.
A question worth separating out:
Q: How should teams combine AI agent monitoring with identity governance controls?
A: Teams should anchor agent monitoring to lifecycle governance, access oversight, and privileged control paths so identity state is reviewed alongside behaviour. That approach helps close the gap between who or what the agent is authorised to be and what it is actually doing in production. The goal is one operating model, not separate security silos.
👉 Read our full editorial: Continuous context is becoming the baseline for AI agent security
12/06/2026 3:41 am
Continuous context is the right control model for AI agents because risk now develops across state, not moments. Periodic scans assume the environment is stable long enough to observe, classify, and act. Zenity’s framing shows that AI agents mutate through configuration changes, memory updates, and chained interactions, so the control plane must track the sequence rather than the snapshot. Practitioners should understand that stateful AI behaviour cannot be governed like a static workload.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- 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.
A question worth separating out:
Q: How should teams combine AI agent monitoring with identity governance controls?
A: Teams should anchor agent monitoring to lifecycle governance, access oversight, and privileged control paths so identity state is reviewed alongside behaviour. That approach helps close the gap between who or what the agent is authorised to be and what it is actually doing in production. The goal is one operating model, not separate security silos.
👉 Read our full editorial: Continuous context is becoming the baseline for AI agent security
