Giskard Guards

Protect your AI agents with context-aware guardrails

Built with your team, configured by AI security experts to fit your business context.

Why model guardrails fail enterprise AI deployments

Generic filters over-block good requests, miss real attacks, and break under agentic workflows.

Context-blindness creates false positives

A banking customer reporting credit card fraud gets blocked as a content violation'. That's not security, that's a broken product.

Up to 40% of blocked requests are false positives with generic guardrails. 
Source: Giskard, RealHarm paper

Traditional guardrails are not built for AI agents

Industry benchmarks use prompts like “forget previous instructions”. Real attacks look nothing like that.

A benchmark that only tests obvious jailbreak strings will always miss the attacks that matter in production.

Traditional AI governance  can't keep up

Your compliance team updates policies in spreadsheets. Your AI team deploys new agents weekly.

By the time governance catches up, the system has already changed three times. This gap is where incidents happen.

Policy-driven decisions for AI agents

Guards inspects context, enforces policy, and blocks unsafe actions before they reach production.

Protect your agent

Red-teaming boosted

Giskard detectors don't just match keywords, they understand the intent and context of every interaction.

Define custom guidelines in natural language, and the detector enforces them semantically.

Quality-first guardrails

Beyond blocking attacks, Giskard Guards ensures your AI responses are accurate, grounded, and on-topic. Bad quality erodes user trust just as much as a security breach.