AI Discovery & Inventory
Gain visibility into models, agents, applications, data, tools, owners, dependencies and use cases across the organization—helping identify shadow AI and establish a system of record.
Prove your AI behaves as intended.
Continuously test, verify, and monitor every AI system in the workflows your teams already use.
Trusted Partners
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
Understand your Agentic Risk as each Agent connects across your stack
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.
Discover AI systems, test them under realistic conditions, verify controls, and continuously monitor changing behavior in production.
AI Security Testing & Red Teaming
Identify vulnerabilities across models, agents, RAG pipelines, tools, memory and workflows before deployment. Test for prompt injection, jailbreaks, data leakage, privilege escalation, tool misuse and control bypass.
AI Attack Simulation
Simulate real-world attacks using adversarial personas, threat actors and operational scenarios. Continuously evaluate how AI systems respond to changing threats, multi-step attacks and compromised agents.
AI Safety & System Evaluation
Evaluate the complete AI system—not just the model—for harmful behavior, bias, hallucination, fragility, resilience and operational failure. Test across user cohorts, environments, edge cases and long-horizon workflows.
Agentic AI Security
Test what AI agents can access, decide and do. Evaluate identity, permissions, autonomy, tools, memory, agent-to-agent interactions and human oversight across Optica’s 4A framework: Access, Autonomy, Action and Accountability.
AI Governance, Risk & Compliance
Define controls, classify AI risk and map test results to organizational policies and regulatory frameworks, including NIST AI RMF, ISO/IEC 42001, OWASP, MITRE ATLAS and the EU AI Act.
AI Assurance & Release Evidence
Verify that required controls are operating before deployment. Each test run produces a signed, traceable Evidence Pack containing results, vulnerabilities, mitigations, residual risk and release-readiness evidence.
AI Observability
Observe prompts, responses, agent decisions, tool calls, data access, memory changes and workflow outcomes across production AI systems. Establish behavioral baselines and investigate changes over time.
AI Runtime Security
Detect and respond to prompt injection, anomalous tool use, unauthorized access, data exfiltration, memory manipulation and policy violations while AI systems are operating in production.
AI Behavioral Monitoring
Measure behavioral trajectory, model fragility, control degradation and accelerating risk using deterministic equations—not an LLM judge. Trigger alerts and targeted retesting when behavior moves outside approved boundaries.
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