Topic starter
02/08/2026 11:53 am
TL;DR: AI security is moving beyond model-centric controls because agentic systems operate at OS level, retain persistent context, and can bypass network-based DLP, according to Cyberhaven. Treating AI security as an extension of traditional cybersecurity leaves blind spots where data, access, and model integrity intersect.
Editorial analysis by NHI Mgmt Group, based on content published by Cyberhaven: “AI Security Best Practices: The Complete Guide”.
By the numbers:
- 39.7% of AI interactions involve sensitive data, according to Cyberhaven.
- Roughly half of all developers were using desktop-based coding assistants by late 2025, according to Cyberhaven.
- Nearly 23% of enterprises had adopted agent-building platforms by early 2026, according to Cyberhaven.
Key questions
Q: What breaks when AI controls depend on replacing the browser?
A: Control coverage weakens when security depends on forcing employees into a new browser.
Q: Why do autonomous AI agents create more access risk than task bots?
A: Autonomous AI agents create more access risk because they can combine persistence, reactivity, and initiative.
Q: What signs show that agentic AI is escaping identity governance?
A: The clearest signs are agents appearing without inventory, connectors being added without review, and permissions that remain broad after deployment.
Practitioner guidance
- Map agentic systems as non-human identities Inventory every desktop agent, coding assistant, and autonomous workflow that can read files, call APIs, or spawn processes.
- Move enforcement to the endpoint layer Add endpoint visibility that can observe process-level behaviour, local file access, clipboard use, and API invocation by agents.
- Classify agent memory as governed data Treat local context windows, cached prompts, and interaction histories as controlled data stores with retention limits, access restrictions, and review requirements.
Bottom line: AI security fails when organisations keep applying browser and network controls to agents that execute locally with persistent context.
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02/08/2026 1:01 pm
Agentic AI security is now an identity governance problem, not just a model-risk problem. Once software can act continuously with delegated access, the control question becomes who or what can act, for how long, and with what scope. That is the same governance logic IAM and PAM teams use for human admins and service accounts, but the actor is now an agent. Practitioners should govern agents as high-risk non-human identities, not as mere application features.
A few things that frame the scale:
- 28% of secrets incidents now originate outside code repositories, in Slack, Jira, and Confluence, and are 13% more likely to be categorised as critical than code-based leaks, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: Should organisations treat AI service sessions like privileged non-human identities?
A: Yes, when those sessions can consume paid usage, call tools, or reach sensitive data. They should be owned, monitored, and reviewed as governed identities, because their operational value and blast radius can be as high as a service account or API token.
👉 Read our full editorial: AI security best practices are failing at the agentic AI edge