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11/08/2026 3:10 pm
TL;DR: Homegrown AI agents need continuous discovery, least-privilege scoping, context-layer guardrails, tool-call validation, human kill switches, and auditability before production, according to Akto's July 2026 analysis and incident references. The operational gap is no longer whether agents can be built, but whether their authority, actions, and shutdown paths can be governed fast enough to prevent material harm.
NHIMG editorial — based on content published by Akto: Top AI security criteria for homegrown agents before production
By the numbers:
- 63% of organizations cannot enforce purpose limitations on their AI agents.
- 60% of organizations cannot quickly terminate a misbehaving AI agent.
- 33% of organisations report their AI agents have accessed inappropriate or sensitive data beyond their intended scope.
Questions worth separating out
Q: How should security teams govern agentic AI as it moves into production?
A: Security teams should govern agentic AI as a class of non-human identity, not as a generic application feature.
Q: Why do homegrown AI agents create more governance risk than purchased tools?
A: Homegrown agents usually inherit whatever controls the builders remembered to include, which makes their governance uneven and hard to verify.
Q: What breaks when runtime guardrails are missing from agentic AI?
A: Without runtime guardrails, the organisation loses control at the moment the agent takes a real-world action.
Practitioner guidance
- Implement continuous agent discovery Build a live registry of every homegrown agent, its owner, connected systems, data sources, and approval status.
- Enforce least privilege at the tool layer Scope each agent to the minimum tool, dataset, and action set required for the task.
- Require pre-execution tool-call validation Check every tool invocation against declared authority before it runs, including arguments, resource scope, and action type.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- The full criterion-by-criterion breakdown for evaluating homegrown AI agents before production
- The incident references and control mappings that justify each requirement in the checklist
- The regulatory alignment discussion for EU AI Act Article 10 and related evidence expectations
- The practical scorecard format used for self-assessment and internal programme review
👉 Read Akto's full analysis of security criteria for homegrown AI agents →
Homegrown AI agent security criteria: what teams should enforce now?
Explore further
11/08/2026 3:25 pm
Homegrown AI agents are becoming non-human identities before most organisations treat them that way. Once an agent can retrieve data, invoke tools, and trigger actions, it has crossed from model output into delegated identity behaviour. That means traditional application review is not enough, because the governance problem is authority assignment, not just software delivery. Teams should manage these systems as identities with runtime permissions, lifecycle controls, and revocation duties.
A few things that frame the scale:
- 63% of organizations cannot enforce purpose limitations on their AI agents, according to Ultimate Guide to NHIs , 2025 Outlook and Predictions.
- 33% of organisations report their AI agents have accessed inappropriate or sensitive data beyond their intended scope, according to AI Agents: The New Attack Surface report.
A question worth separating out:
Q: Who is accountable when an AI agent accesses sensitive data it was not meant to use?
A: Accountability sits with the team that approved the agent, its connectors, and its policy boundaries, not with the runtime behaviour alone. Organisations need ownership for intent, permissions, monitoring, and validation so they can prove whether the agent stayed inside its approved purpose. Without that, audit and regulatory response become retrospective guesswork.
👉 Read our full editorial: Homegrown AI agent security criteria are now a production gate
