Topic starter
24/06/2026 7:29 pm
TL;DR: Enterprise systems are shifting from model centrality to contextual determinism, with Collibra arguing that the LLM is becoming interchangeable while governed context is the real differentiator. That changes the security conversation from model selection to lifecycle control, access boundaries, and operational governance across data, agents, and orchestration.
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: 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.
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.
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.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
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: Context engineering is the new enterprise control plane