Topic starter
24/06/2026 7:29 pm
TL;DR: The real risk shifts to uncontrolled enterprise memory across workflows and agents, because Collibra argues the context layer now shapes what AI can expose and reuse. Enterprise systems need tighter rules for sourcing, sharing, and retiring context to keep that memory traceable and bounded.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “Models are commodities, context is proprietary: Why context engineering is the new business standard”.
Key questions
Q: Why does too much context increase security risk?
A: Too much context increases security risk because it expands both the information a system can expose and the number of downstream workflows that can act on it.
Q: How can organisations tell whether contextual access decisions are improving governance?
A: Look for fewer routine exceptions, faster certification cycles, clearer audit trails, and reduced reviewer fatigue.
Q: How should security teams implement governed context for AI agents in enterprise environments?
A: Security teams should place a governance layer between agents and source systems so the agent receives trusted meaning, policy, and lineage with the data.
Practitioner guidance
- Inventory context sources and owners Map every system that contributes context to AI workflows, including CRM, ERP, collaboration tools and telemetry stores.
- Set retrieval boundaries by task Define the minimum effective context for each use case and block unnecessary cross-domain enrichment.
- Separate model selection from context control Create governance that allows model substitution without reauthorising the underlying enterprise memory layer.
Bottom line: Enterprise AI security is shifting from model-centric thinking to control of the context layer that feeds the model.
Glossary terms: Context Engineering, Enterprise memory, Context Overload, Signal Density
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This topic was modified 21 hours ago by NHI Mgmt Group
25/06/2026 4:47 am
Context engineering is becoming a governance problem before it is a modelling problem. The article correctly shifts attention away from which LLM is in use and toward who can assemble, enrich and distribute the context that drives outcomes. That matters because enterprise memory is now an operational asset with access boundaries, lifecycle decisions and leakage paths. Practitioners should treat context as governed material, not as an informal prompt adjunct.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: What is the difference between model selection and context control?
A: Model selection chooses the reasoning engine, while context control governs the information that shapes the outcome. A better model does not compensate for weak context boundaries, and a swapped model should not require redesigning the enterprise memory layer. For practitioners, the decisive control is the one over access to context, not the brand of model.
👉 Read our full editorial: Enterprise context governance is the real AI security control
This post was modified 21 hours ago by NHI Mgmt Group