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06/09/2026 6:21 pm
TL;DR: Security for AI and AI for Security should be run as separate programmes that share an evidence bus, with policy at the boundary, schema-first outputs, provenance for retrieval, and continuous evaluation in CI and on shadow traffic, according to LEVO. The core shift is from model-call experimentation to routed systems with auditable controls, because the highest risk now sits where model text turns into tool action.
NHIMG editorial — based on content published by LEVO: a practical narrative that connects AI risk, controls, and value so leaders can fund, ship, and scale AI with confidence
By the numbers:
- Lack of credential rotation is cited as the top cause of NHI-related attacks by 45% of organisations, followed by inadequate monitoring and logging and over-privileged accounts at 37% each.
Questions worth separating out
Q: How should security teams set boundaries for AI-assisted decisions?
A: Security teams should separate tasks AI can accelerate from decisions that carry accountability, approval, or risk acceptance.
Q: Why do organisations need provenance controls for AI training data?
A: Because provenance tells you where data came from, who changed it, and whether it should have been trusted in the first place.
Q: What are the signs that AI governance is failing in the enterprise?
A: Common warning signs include rapid growth in AI use without matching policy coverage, sensitive files being copied into personal accounts, and a large share of AI apps carrying high or critical risk.
Practitioner guidance
- Implement a gateway on every effectful AI route Enforce policy, schema validation, budgets, approvals, and trace export before any tool or API call can execute.
- Adopt schema-first outputs for all AI workflows Require typed responses for structured tasks and block malformed or ambiguous outputs from reaching downstream systems.
- Sign corpora, indexes, and source manifests Attach source IDs to retrieved content, keep manifests under change control, and define takedown procedures for material that loses licensing or trust validity.
What's in the full article
LEVO's full article covers the operational detail this post intentionally leaves for the source:
- Gateway policy patterns for routed AI systems, including schema checks, approval gating, and trace export
- Minimum evidence bundle contents for audits, change control, and rollback planning across AI workflows
- Operational scorecard design for grounding, schema pass rate, block rate, and replayable evidence
- Implementation guidance for budgets, caching, routing, and context limits that affect cost and risk
👉 Read LEVO's framework for AI security gateways, provenance, and assurance →
AI security at the boundary: what practitioners need to govern?
Explore further
06/09/2026 6:22 pm
Policy at the boundary is the new security primitive for AI systems. The article correctly places the decisive control point between model output and action, not inside the model alone. That boundary is where schema checks, approvals, budgets, and trace export convert unsafe language into governable behaviour. For practitioners, this means AI controls need to sit in runtime enforcement, not only in development review.
A question worth separating out:
Q: What should teams do when an AI workflow can influence production actions?
A: Require explicit approval boundaries, deny-by-default tool access, and traceable logging for every action path. Separate content generation from execution wherever possible, and review exception handling carefully because attackers often target the human or workflow bypass rather than the model itself.
👉 Read our full editorial: AI security needs gateway policy, provenance, and continuous assurance
