
AI SecurityCloud Security
Operant AI
Runtime protection for generative-AI apps, agents, and APIs that blocks prompt injection and data exfiltration inline across cloud-native environments.
Operant AI Overview
Operant AI is a runtime application protection platform that secures generative-AI workloads, APIs, and cloud-native applications from a single layer. It installs without code changes through a Helm chart and uses a 3D Runtime Defense model that discovers live AI and API assets, detects attacks inside running workloads, and blocks them inline. Its focus is the runtime layer, where prompts, agent actions, and API calls actually execute, rather than static scanning.
The platform groups its coverage into runtime modules. AI Gatekeeper inspects live large language model (LLM) and agent traffic, blocking prompt injection, unauthorized AI behavior, and data exfiltration while governing Model Context Protocol (MCP) servers and non-human identities. Endpoint Protector finds shadow AI and secures coding assistants and developer AI tools, and Agent Protector extends controls to agentic workflows. Sensitive data is auto-redacted in line before it reaches a model, and detections map to the OWASP LLM Top 10.
Operant AI is SOC 2 Type II compliant and contributes to the CNCF, the OWASP Foundation, and the Coalition for Secure AI. Founded in 2021 and based in San Francisco, the company raised a 10 million dollar Series A led by SineWave Ventures and Felicis. It serves cloud-native engineering and security teams adopting AI.
Key Capabilities
mapped to solution categoriesDetects 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.
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.
Analyzes AI runtime behavior to surface prompt injection, anomalous data access, and model extraction as posture findings, exporting scores and telemetry to SIEM and SOAR rather than blocking inline.
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.
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.
Compliance
certificationsIntegrations
compatible toolsImplementation & support
Info last updated on June 26, 2026