Notifications
Clear all
Topic starter
20/08/2026 5:55 pm
TL;DR: Generative AI introduces emergent capability, explainability gaps, and security risks such as data poisoning, misuse, and hallucination, while 44% of summit attendees said privacy and security were their biggest concern, according to Fiddler. The governance challenge is no longer whether LLMs can scale, but whether MLOps, auditability, and control boundaries can keep pace.
NHIMG editorial — based on content published by Fiddler: Enterprise Generative AI - Promises vs Compromises
By the numbers:
- 44% were concerned with privacy and security issues when asked about incorporating LLMs into business applications.
Questions worth separating out
Q: How should security teams govern generative AI once it becomes part of daily operations?
A: Treat generative AI as an access-bearing workflow, not a standalone tool.
Q: Why do large language models create new security risks as they scale?
A: Larger models can show emergent behaviour, which means new capabilities appear in ways that are not proportional to simple size metrics.
Q: How do you know whether AI output controls are working?
A: Output controls are working when teams can trace a response back to its prompt, model version, retrieval context, and any downstream action.
Practitioner guidance
- Define model approval gates Require explicit review before a model, prompt workflow, or retrieval pipeline can move into production, and base approval on tested behaviour, logging coverage, and owner accountability.
- Instrument provenance logging Capture prompts, model versions, retrieval inputs, and downstream actions so investigators can reconstruct what happened when an output is disputed or harmful.
- Scope AI system access like a privileged workload Limit service accounts, API keys, and tool permissions used by AI systems to the minimum required, and revoke credentials when the workflow changes or ends.
What's in the full article
Fiddler's full blog post covers the operational detail this post intentionally leaves for the source:
- The specific summit discussion points from Dr. Ali Arsanjani on how Google is updating the MLOps lifecycle for generative AI.
- The article’s deeper explanation of emergent behaviour, including the relationship between model size, data efficiency, and capability leaps.
- The source’s fuller treatment of AI security concerns such as data poisoning, dual use, distribution shift, and human control over deployed systems.
- The audience response data and surrounding context on privacy and security concerns in enterprise LLM adoption.
👉 Read Fiddler's analysis of enterprise generative AI promises and compromises →
Enterprise generative AI risk: what IAM and security teams miss?
Explore further
20/08/2026 6:35 pm
Generative AI governance debt is the hidden cost of rapid adoption: teams often scale model use faster than they can define accountability, logging, and review boundaries. Once LLMs enter business workflows, the control problem shifts from model quality alone to operational traceability, approval boundaries, and incident reconstruction. That is why AI governance must be treated as a programme discipline, not a one-time launch check.
A question worth separating out:
Q: What should organisations do when agentic AI starts using enterprise tools?
A: Organisations should define what the system may access, what actions require approval, and who is accountable if behaviour changes during execution. The key is to govern runtime authority, not just initial provisioning. Without that boundary, the AI workflow can expand its own operational reach faster than conventional IGA can observe it.
👉 Read our full editorial: Enterprise generative AI security needs governance, not just scale
