Operant AI

Detects and blocks adversarial inputs designed to override system prompts, extract training data, or redirect model behavior. Detection approaches include pattern matching, input semantic analysis, and secondary model classification.

Intercepts prompts and completions to prevent sensitive data (PII, credentials, internal IP), from being transmitted to external LLM services or returned in model responses.

Enforces IAM-style policies on LLM API access, controlling which users and applications can invoke which models and data sources, with audit logging.

Evaluates model outputs against content policy, data classification rules, and format expectations before delivery to end users, blocking responses containing sensitive data or policy violations.

Records prompts, completions, and metadata for all AI interactions with tamper-resistant storage, supporting compliance, forensics, and policy investigation.

Discovers, governs and allowlists the Model Context Protocol servers and tools that AI agents are permitted to invoke.

Automatically discovers AI models, LLM API connections, ML pipelines, and AI-enabled SaaS applications in use across the organization, including those deployed without IT authorization.

Detects sensitive or regulated data in AI training, fine-tuning, or third-party LLM flows without appropriate controls, such as unencrypted PII in inputs or PHI sent to external APIs.

Discovers AI model and inference endpoints and flags public exposure, weak authentication, default credentials, or excessive permissions as posture misconfigurations.

Maps data lineage and provenance across AI training and inference pipelines, tracing how PII, PHI, and IP move into models and external services.

Assesses the identities and service accounts that AI models, pipelines, and agents use, flagging over-permissioned non-human identities and access paths that violate least privilege. Reports identity risk as a posture finding, distinct from enforcing access policies at the model API at runtime.

Discovers and enforces least-privilege access for non-human and AI-agent identities across systems and data.

Monitors AI-agent behavior at runtime to detect anomalous or malicious actions and policy violations.

Detects attacks at the application and API layer at runtime using behavioral signals such as unexpected process behavior, suspicious API calls, unusual service-to-service communication, and exploit activity across cloud apps, containers, and Kubernetes.

Detects and blocks malicious API behavior at runtime using anomaly and behavioral analysis trained on attack patterns.

Continuously discovers and inventories all APIs across the environment, including shadow and zombie APIs that are not tracked in the official catalog.