Prism AI Observability
AI observability built for compliance teams.
Every LLM call captured, scored, and stored with PII scrubbed before it lands in your database. Regulator-ready exports in under 60 seconds.
- #4821Credit Risk Query1.3s5/5PII redacted
- #4820Underwriting Decision0.9s3/5Drift detected
- #4819Policy Lookup0.4s5/5Grounded
Audit pack ready
47 traces · 60s export
Prism
Break your AI before someone else does
Structured adversarial testing to find prompt injection vulnerabilities, guardrail bypasses, and unsafe behaviors, before they reach production.
- Prompt injection: system-prompt override and instruction extraction
- Guardrail bypass: encoding tricks, language switching, semantic rephrasing
- Information extraction: training data, system config, cross-tenant probes
- Multi-turn escalation: gradual context shift over long conversations
The problem
Your guardrails pass every test you wrote. But you wrote the tests, so you tested what you expected. Adversarial users, automated attacks, and creative edge cases will find the gaps you did not imagine. Red teaming systematically probes your AI system for failure modes that standard evaluation misses.
Capabilities
What you get with Prism
Prompt injection testing
Systematic attempts to override system instructions, extract system prompts, and manipulate model behavior through crafted inputs, across known patterns and novel variations.
Guardrail bypass testing
Probe each guardrail rule with evasion techniques: encoding tricks, language switching, semantic rephrasing, multi-turn escalation. Verify enforcement holds under pressure.
Information extraction
Attempts to extract training data, system configuration, other users' data, or knowledge-base contents that should not be accessible.
Policy boundary testing
Interactions designed to push the model to the edge of content policy, verifying that 'almost violating' doesn't cross into 'actually violating' under realistic conditions.
Multi-turn escalation
Conversations that gradually shift context over many turns, testing whether guardrails remain effective when conversation history is long and complex.
Red team report
Structured deliverable: test methodology, attack categories, success / failure rates per guardrail, identified vulnerabilities, and remediation recommendations.
How it works
From instrumentation to evidence
- 1
Define scope
Set which agents, which guardrails, which attack categories, and what constitutes a failure.
- 2
Run adversarial suites
Execute a combination of automated attack patterns and manually crafted probes targeting your domain-specific risks.
- 3
Review results
Identify which attacks succeeded, which guardrails held, and where enforcement gaps exist.
- 4
Remediate and re-test
Tighten guardrail rules, add new detection patterns, adjust model system prompts, and re-test to verify fixes.
What teams use it for
In production, every day
Pre-launch hardening
Probe each guardrail rule with evasion techniques before a new agent or assistant goes live to external users.
Continuous adversarial coverage
Re-run suites after prompt changes, model upgrades, or guardrail edits to catch enforcement regressions.
Security and audit reviews
Provide structured evidence of adversarial testing for security review boards and regulatory submissions.
Attack surface
What red teaming covers
Prompt injection testing
Systematic attempts to override system instructions, extract system prompts, and manipulate model behavior, across known patterns and novel variations.
Guardrail bypass testing
Probe each rule with encoding tricks, language switching, semantic rephrasing, and multi-turn escalation to verify enforcement holds under pressure.
Information extraction
Attempts to extract training data, system configuration, other users' data, or knowledge-base contents that should not be accessible.
Policy boundary testing
Interactions designed to push the model to the edge of content policy, verifying that almost-violating does not cross into actually-violating under realistic conditions.
Multi-turn escalation
Conversations that gradually shift context over many turns to test whether guardrails remain effective when history is long and complex.
Deliverable
Red team report
A structured report documenting test methodology, attack categories, success / failure rates per guardrail, identified vulnerabilities, and remediation recommendations, suitable for security review boards and regulatory submissions.
Regulatory alignment
Built for AppSec, ML Engineering Leads, CISOs
Related capabilities
LLM Observability: Trace Logging Built for Compliance
Structured traces give you the full story of what your AI said, why it said it, how long it took, and what it cost.
LLM Guardrails: PII Redaction and Prompt Injection Blocking
Real-time detection and enforcement for PII, PHI, prompt injection, content policy violations, and off-topic responses, scoped per agent, per project, per knowledge base.
LLM Evaluations: Five-Dimension Automated Quality Scoring
Define quality rubrics, score every interaction, and catch regressions before users do, with automated evaluators that run on every trace or on a schedule you control.
Prism X: AI DLP for Employees Using ChatGPT, Claude, Gemini
Prism X enforces data loss prevention policy in the browser, before prompts and uploads reach third-party AI services. Signed policy, real-time enforcement, audit-grade events.
Start tracing in 5 minutes
One SDK. Five minutes. Full audit trails, PII redaction, and guardrail enforcement, from day one.