Unapproved AI tool usage by employees, driven by productivity needs, creates significant visibility and governance challenges for companies.
Traditional security tools that rely on pattern matching are insufficient for AI because they cannot interpret the meaning or context of prompts and data.
An AI control plane acts as a central governance layer, similar to an API gateway, to manage interactions between users, models, and enterprise data.
A robust AI control plane must integrate identity, perform deep prompt inspection, and use a nuanced policy engine to make context-aware decisions.
Effective governance requires advanced features like dynamic model routing, permission-aware RAG, human approval workflows, and semantic logging for full auditability.
AI agents that can execute actions and call tools fundamentally change the security model from a data leakage risk to an infrastructure exposure problem.
A successful prompt injection attack against an AI agent can escalate from generating an unsafe answer to executing an unauthorized and potentially harmful action.
Secure agents require explicit, action-level authorization and a runtime enforcement layer that validates every proposed action against security policies before execution.
Implementing risk-based human-in-the-loop approvals and ensuring complete audit traceability are critical for safely deploying high-impact AI agents.
Enterprise RAG systems must be treated as an access control problem, ensuring that the retrieval process respects all user and document-level permissions.
Organizations must decide whether to use platform-native controls or build a custom governance layer based on their unique workflows and compliance needs.
The key to successful enterprise AI adoption is not simply using AI, but implementing robust runtime governance to manage it safely and observably at scale.
Jobs that call for the skills explored in this talk.