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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.
NHIMG editorial — based on content published by Cyberhaven: AI Security Best Practices, the Complete Guide
By the numbers:
- 63% of breached organizations either have no AI governance policy or are still developing one.
- 39.7% of AI interactions involve sensitive data.
- 57% of employees use personal GenAI accounts for, s for work purposes.
Questions worth separating out
Q: How should security teams govern employee AI use without blocking productivity?
A: Start with visibility into sanctioned and shadow AI use, then apply runtime policies that inspect intent and context rather than only keywords.
Q: Why do AI agents complicate existing IAM and PAM controls?
A: AI agents complicate IAM and PAM because they often inherit delegated credentials, operate across multiple systems, and keep acting after the initial approval moment has passed.
Q: What do security teams get wrong about AI-generated code risk?
A: They often focus on catching insecure output after code is written, which is too late for AI-native workflows.
Practitioner guidance
- Implement endpoint visibility for AI agents Inventory desktop agents, coding assistants, and local AI processes, then monitor what data they can read, what commands they issue, and which APIs they reach.
- Extend IAM governance to AI tool access Review AI platforms, agent frameworks, and service integrations as governed access paths.
- Build data lineage into AI security controls Classify training data, retrieval documents, embeddings, and cached outputs, then trace where each data set enters and exits AI workflows.
What's in the full article
Cyberhaven's full guide covers the operational detail this post intentionally leaves for the source:
- Step-by-step control mapping for securing generative AI and agentic AI across cloud and endpoint environments.
- Specific examples of AI security controls for DSPM, DLP, model access management, and incident response.
- The article's practical distinctions between browser-bounded tools and OS-level agents, including how to detect each.
- Source-linked benchmarks and implementation framing that help teams translate policy into deployment decisions.
👉 Read Cyberhaven's AI security best practices guide for operational control detail →
Agentic AI security: are your controls keeping up?
Explore further
02/08/2026 1:01 pm
AI security is now an identity and access problem, not just a model-risk problem. Once AI systems can read files, invoke tools, and persist context, the security question shifts from output quality to who or what is authorised to act. That brings IAM, PAM, and NHI governance into the AI stack whether teams planned for it or not. Practitioners should treat agent permissions, service credentials, and delegation paths as first-class control objects.
A question worth separating out:
Q: How can organisations tell whether their AI security model is actually working?
A: They should test whether the control stack can explain who acted, what data was touched, and what purpose the action served. If those three signals cannot be correlated in one incident view, the model is likely monitoring access without governing behaviour. That is a visibility gap, not a complete AI security posture.
👉 Read our full editorial: AI security best practices are failing at the agentic AI edge
