BEFORE DEPLOYMENT
Test the complete system.
AI Range tests models, agents, data, tools, memory, and workflows together under adversarial and operational conditions.
Prove your AI behaves as intended.
Continuously test, verify, and monitor every AI system in the workflows your teams already use.
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THE PROBLEM
AI risk has moved beyond the model.
As agents connect to data, memory, tools, identities, and one another, the most consequential failures emerge in the interactions between them.
AGENT GOAL HIJACKING
Hidden instructions redirect an agent’s objective and turn trusted workflows into attack paths.
MICROSOFT AIRT TAXONOMYTOOL AND PRIVILEGE ABUSE
Agents use legitimate tools, permissions, and credentials to access data or take actions beyond their intended scope.
NIST AGENT SECURITYMEMORY AND CONTEXT POISONING
Malicious or corrupted context persists across sessions and quietly reshapes future decisions.
OWASP AGENTIC TOP 10 · MICROSOFT AIRT TAXONOMYAGENTIC SUPPLY CHAIN COMPROMISE
Compromised models, MCP servers, plugins, APIs, or dependencies inherit trust and access across the system.
OWASP AGENTIC TOP 10 · MICROSOFT AIRT TAXONOMYINSECURE AGENT COMMUNICATION
Spoofed messages and weak identity controls cause agents to trust and act on unauthorized instructions.
OWASP AGENTIC TOP 10 · MICROSOFT AIRT TAXONOMYCASCADING AUTONOMOUS FAILURE
One bad output, action, or signal propagates across connected agents and systems before a human can intervene.
OWASP AGENTIC TOP 10SENSITIVE DATA EXPOSURE
Prompts, retrieval systems, tools, and agent actions expose regulated data, credentials, or proprietary information.
NIST AI RMF · MICROSOFT AIRT TAXONOMYCONFABULATION IN CONSEQUENTIAL DECISIONS
A confident but false output enters a clinical, financial, operational, or public-sector decision workflow.
NIST AI RMF GENERATIVE AI PROFILELOSS OF CONTROL AND DECEPTIVE BEHAVIOR
An AI system evades oversight, subverts monitoring, or continues acting beyond the authority granted to it.
CALIFORNIA TFAIA, SB 53 · NIST AGENT SECURITYMATERIAL INCIDENT BLINDNESS
Without traceable behavior and evidence, leaders cannot quickly determine an incident’s scope, impact, or reporting obligations.
SEC CYBERSECURITY RISK MANAGEMENT RULE · CALIFORNIA TFAIA, SB 53
The wider the interaction surface, the further one failure can travel.
Agent Interaction Surface
Security gap
Undetected vulnerabilities reach production. Remediation happens after the incident, and reactive remediation costs billions.
Observability gap
AI behavior is unexplainable in production. Deployments stall, approvals drag, and first-mover advantage evaporates.
Governance gap
Policies exist on paper. When a regulator asks for proof the governed system actually behaves, no artifact answers the question.
Agentic risk lives in the seams, and those seams are where traditional testing stops.
SOLUTIONS
Test before deployment. Measure what changes after.
AI Range tests the complete AI system and produces signed evidence for release decisions. Peregrine measures changing behavior in production with deterministic equations. Together, they provide continuous assurance from pre-deployment through runtime.
RELEASE DECISION
Verify the controls. Record the results.
Each run is scored against defined controls and captured in a signed, traceable Evidence Pack.
IN PRODUCTION
Measure changing behavior.
Peregrine tracks behavioral trajectory, model fragility, and accelerating risk using deterministic equations, not an LLM judge.
FOUR A'S × SCORECARD DIMENSION
Behavior scored against the controls that govern it.
RELEASE OUTPUTREVIEW REQUIRED
Safety
Accuracy
Policy Compliance
Permission Integrity
Risk Exposure
Access
0FAIL0WARN
Autonomy
0FAIL0WARN
0FAIL0WARN
Action
0FAIL0WARN
0FAIL0WARN
0FAIL0WARN
0FAIL0WARN
Accountability
0FAIL0WARN
0FAIL0WARN
2 FAIL5 WARN2 PASS
AI RANGE | AI ASSURANCE PLATFORM
Test the system you deploy, not just the model.
AI Range tests models, agents, data, tools, memory, and workflows together, then produces a signed, audit-ready Evidence Pack for every run.
Discover
9-section intake. Readiness Score 0–100.
Map
Topology graph. MITRE ATLAS classification.
Test
Executor Swarm. MICE adversarial scenarios.
Evaluate
Risk Severity, Likelihood, and 5-dimension scorecard.
Verify
OPA Rego. 21 controls, 7 families.
Validate
Evidence Pack PDF + JSON. SHA-256.
◆ THE EVIDENCE PACK
A signed, per-run compliance artifact with a SHA-256 chain of custody. It records what the system was asked, what it did, what passed, what failed, and which controls verified it. This is what an examiner sees.
NIST AI RMFOWASP LLM TOP 10MITRE ATLAS
PEREGRINE | RUNTIME BEHAVIOR MONITORING
Measure where AI behavior is heading.
Peregrine calculates risk with deterministic equations, not an LLM judge, revealing behavioral drift, model fragility, and acceleration toward a guardrail break.
Observed behaviorProjected trajectoryGuardrail threshold
THE MATH IS THE MATH.
AI output can vary. The equations used to score it do not.
GUARDRAIL EROSION VELOCITY
Measures whether conversational risk is accelerating toward a guardrail break, turn by turn.
PHI SCORE
Measures how much risk the model amplifies from the user’s input.
ROBUSTNESS INDEX ρ
Shows whether a model absorbs adversarial pressure or amplifies it.
AI Range tests behavior before deployment. Peregrine measures it in production. Together, they provide continuous assurance across the AI lifecycle.
Explore PeregrineTest the systemObserve the behaviorVerify the controlsProve the outcome