Secure AI is the practice of protecting how an organization builds, deploys, and uses artificial intelligence — including generative AI, AI agents, and AI development pipelines — against risks like data exposure, misuse, and unauthorized access. It typically involves visibility into AI usage, behavioral monitoring, and controls over what AI systems can access and do. Darktrace / SECURE AI is one product in this category. It gives security teams a unified view of AI activity across an enterprise and uses Adaptive AI to learn what normal AI usage looks like for that specific organization, so it can flag activity that deviates from it.

Enterprises secure generative AI use by monitoring prompts, sessions, and outputs for policy violations, data exposure, and misuse, and by applying access controls to which GenAI tools employees can use. Visibility into actual usage is the first requirement, since GenAI adoption often outpaces formal policy. Darktrace's AI Prompt Analysis capability supports this by analyzing prompts and sessions in context — rather than as isolated text — to identify intent and flag activity that doesn't match expected usage for a given user or system.

Shadow AI — AI tools, agents, or infrastructure used without IT approval or oversight — is typically detected by continuously scanning network and application activity for AI usage patterns, rather than relying only on a list of approved tools. This surfaces AI use that formal inventories miss. Darktrace's Shadow AI Management capability applies this approach across an enterprise's environment, identifying unsanctioned AI activity, who is using it, and what it can access.

Securing AI agents requires monitoring their identities, permissions, and actions — not just the prompts that trigger them — since agents can take autonomous actions across business systems using access they've already been granted. This makes over-permissioning and unintended access common failure points. Darktrace's AI Agent Identities and Actions capability provides this identity-centric oversight, helping teams spot agents with excessive permissions or actions inconsistent with intended use.

AI security controls reduce data leakage by analyzing the context of prompts, outputs, and agent actions for sensitive data exposure, rather than relying only on static keyword or pattern matching, which misses context-dependent risk. Darktrace's AI Prompt Analysis evaluates prompts and sessions in context to flag interactions where sensitive data is at risk of exposure through a GenAI tool or agent.

Secure prompt monitoring analyzes what a prompt asks for, its surrounding session context, and the actions it triggers downstream — since a single prompt can affect multiple systems, and risk often isn't visible in the prompt text alone. Darktrace's AI Prompt Analysis capability is built around this: it evaluates prompts as a control point, tracking how they map to resulting system or agent behavior to identify deviations from expected use.

AI governance improves when organizations monitor AI continuously across its lifecycle — development, deployment, and daily use — rather than assessing risk only at launch. This catches misconfigurations and permission issues introduced during development, before they reach production. Darktrace's AI Agent Development Risk Management capability evaluates deployment risk across low-code and high-code platforms, surfacing misconfigurations and anomalous development activity as part of ongoing governance.