See What Your Agents Are Actually Doing. In Real Time.
Autonomous agents take thousands of actions and can deviate from approved behavior at any time of day (not just working hours). Prediction Guard gives you full visibility into every policy violation, model call, tool invocation, and data access. You can know what's happening while it's happening, not weeks later during an incident reconstruction.
Send AI Security Events to Your Existing Security and Monitoring Infrastructure
AI security events don't need a new dashboard nobody checks. Prediction Guard streams every logged action, policy violation, and anomaly directly into the tools your security team already uses (Grafana, Splunk, Datadog, etc.). Agent behavior shows up alongside the rest of your infrastructure telemetry, correlated with the same alerting and response workflows you already run.
Why it matters: A separate AI monitoring tool is a blind spot with extra steps (one more login, one more alert queue, one more place an incident can get missed because nobody was watching it in real time). Feeding AI security events into infrastructure your team already monitors means agent activity gets the same attention, the same correlation, and the same response speed as everything else you're already defending.
When Policy Blocks an Action, You See Exactly Why
When an agent encounters a policy violation, Prediction Guard doesn't just silently deny it. It returns a clear, human-readable error identifying what was blocked, which rule triggered the block, and where in the request the violation occurred. Developers get an understandable error instead of a confusing failure. Security teams get an immediate, attributable record instead of a gap in the log.
Why it matters: A blocked action that fails silently just looks like a bug. Someone spends an hour debugging an integration issue that was actually an attempted attack. Surfacing the violation clearly, in real time, turns every blocked attempt into immediate signal instead of noise: your team knows the difference between "the API is broken" and "someone just tried to inject instructions into your agent."
Know Who's Using What — Before It Becomes a Problem
Beyond security events, Prediction Guard gives you a live view of usage across your entire AI stack: which API keys, agents, and developers are calling which models and MCP servers, how often, and at what cost. Token consumption, request volume, and resource usage are all attributed down to the individual agent or credential.
Why it matters: A runaway agent stuck in a retry loop, a developer's test script left running against a production model, a single misconfigured integration silently burning through your token budget. Prediction Guard can help surface the spike while it's happening, tied to the exact agent or key responsible, so you can act before it shows up as a surprise on next month's bill.
Comprehensive Action Logging, Tied to Agent Identity
Every tool invocation, model interaction, and data access is logged with full context (agent identity, timestamp, triggering request, and outcome). Nothing an agent does happens off the record.
Why it matters: Investigating a compromise without complete logs means guessing. Investigating one with them means answering, precisely, what happened, when, and under whose authority.
Scan for Risk. Know How Vulnerable You Are.
Prediction Guard has active model scanning functionality that runs models through a battery of injection resistance and safety tests, scoring susceptibility to prompt injection, jailbreaks, and unsafe outputs. These evaluations run against private, internally-maintained test sets, not public benchmarks (because public benchmark data should be considered compromised the moment it's published).
Why it matters: Private, continuously-refreshed test sets remove that bias, giving you a risk score that reflects actual vulnerability instead of benchmark familiarity.
