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02/08/2026 12:10 pm
TL;DR: Gartner predicts 40% of enterprise applications will integrate task-specific AI agents by 2026, while 75% of leaders already name governance and security as the main deployment challenge, according to Straikerai. The real issue is not agent adoption itself but the assumption that pre-deployment controls can contain runtime behaviour once agents start chaining tools and actions.
NHIMG editorial — based on content published by Straikerai: The Agent Security Gap: Why 75% of Leaders Won't Let Security Concerns Slow Their AI Deployment
By the numbers:
- Gartner predicts 40% of enterprise apps will integrate AI agents by 2026.
- Despite 75% of leaders citing security as their top concern, organizations are deploying anyway because waiting means falling behind.
Questions worth separating out
Q: How should security teams authorize AI agents that can chain multiple actions?
A: Security teams should move beyond static allow or deny decisions and evaluate the agent’s purpose, context, and expected outcome at runtime.
Q: Why do AI agents complicate traditional access reviews?
A: AI agents complicate access reviews because they can accumulate permissions across tools and environments faster than manual certification cycles can observe.
Q: What breaks when AI controls stop at pre-deployment testing?
A: Pre-deployment testing cannot stop a compliant model from making risky decisions in a live workflow or through connected tools.
Practitioner guidance
- Define agent-specific access scopes Document the exact systems, data classes, and tools each agent may touch, then block any cross-domain access that is not explicitly justified by the use case.
- Add runtime monitoring for tool use Track every agent tool invocation in real time so that unusual API calls, unexpected data access, or chained actions can be flagged before the workflow completes.
- Require behavioural audit trails Capture the sequence of prompts, tool calls, data reads, and outputs so investigators can reconstruct how an agent reached a sensitive action.
What's in the full article
Straikerai's full post covers the operational detail this post intentionally leaves for the source:
- Examples of runtime security controls that detect unusual agent behaviour during live execution
- The article’s specific breakdown of prompt injection prevention and tool-use validation
- Operational guidance for building audit trails that support investigation and compliance
- The source’s own framing of how to layer pre-deployment testing with production monitoring
👉 Read Straikerai's analysis of the agent security gap and runtime controls →
AI agent deployment is outrunning runtime controls. Are yours ready?
Explore further
02/08/2026 1:14 pm
Runtime security is the missing identity control plane for AI agents. The article correctly shows that pre-deployment testing alone cannot govern active agent behaviour once tools, prompts, and data sources are in motion. In identity terms, the control point has moved from issuance to execution, which is why runtime observation and policy enforcement now define the real security boundary. Practitioners should treat agent execution as a governed identity event, not a software feature.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), 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: Who should be accountable when an AI agent causes a security incident?
A: Accountability should sit with the human owner, platform team, or business function that granted and operated the agent. The identity may act independently, but governance cannot detach responsibility from the delegation chain. Programs should define ownership, escalation, and remediation paths before deployment so responsibility is clear when the agent's behaviour changes.
👉 Read our full editorial: AI agent deployment is outrunning runtime security controls
