Stop dangerous agent behavior at runtime.
AI agents chain tool calls, access sensitive data, and take actions across full sessions. The threat is rarely a single bad prompt. It's a sequence of legitimate-looking actions that combine into dangerous behavior. Noma AI-DR (AI Detection and Response) monitors the full chain of agent actions and enforces security policies in real time across every environment where your agents run.
the Challenge
AI operates in milliseconds, with agents making autonomous decisions across business-critical systems. Static analysis and traditional security tools cannot understand intent, evaluate context, or prevent threats in real time.
Noma Solution
Noma AI Runtime Protection analyzes every AI interaction as it happens, understanding user intent, agent behavior, and execution context. It enforces security, privacy, and compliance policies in real time, stopping threats before they become incidents.
How runtime protection works
Noma enables organizations to discover, govern, test, and protect AI and agents across the enterprise.
Runtime observability into agent behavior
See which skills, MCPs and tools agents actually use, and create a baseline for agent behavior over time. Detect anomalous behavior confidently by understanding the context of each action.
Protection against every kind of risky behavior
Stop risky behavior resulting from adversarial threats against agents, or agents going rogue, misinterpreting intent, and making mistakes. Detectors include prompt injection (including encoded and obfuscated payloads), sensitive data leakage (PII, PCI, secrets) with inline masking, malicious intent, tool poisoning, and more. AI Guardrails let you define custom detection logic in natural language, and every detector is independently tunable for sensitivity and action: monitor, alert, block, or mask.
Context that drives high-fidelity detections
The Noma Runtime Context engine inspects agent interactions across individual events and full sessions, and combines it with posture context like identity, data, and blast radius, baselining behavior over time. This context lets Noma stop real risk while reducing false positives.
Fast to Operationalize
Hundreds of out-of-the-box policies, benchmarks, and protection profiles, tuned by industry, agent function, and agent type, built from deployments with dozens of Fortune 500 security teams help you operationalize runtime security across endpoint, SaaS, and homegrown agents.
Use cases
Part of the Noma platform
AI-DR runs on the Runtime Context Engine, which correlates posture context from AI-SPM with what the agent does at runtime and inspects behavior across five layers: event, session, identity, data, and baseline. Discovery feeds the registry. Access Control defines what each agent is allowed to do, and Red teaming findings inform what policies to set.
Surface all AI assets and their risks
Noma finds every agent, model, MCP server and tool across your cloud, SaaS, and developer environments - often discovering 10 to 100x more agents than teams expect - and surfaces universal risks in your AI estate.
Set and enforce the rules
Define which agents are approved, what data they can access, and what actions they can take - enforcing policies in real time, before actions are carried out.
Test AI apps & agents continuously
Noma’s agents probe your AI apps and agents for prompt injection, jailbreak, data leakage, and goal drift - using sophisticated multi-turn attacks that uncover agent and model weaknesses.
See and stop threats in context
Every agent action looks normal on its own. The threat only appears in context. Noma monitors the full behavioral chain of every agent session (prompts, tool calls, data access, actions) and detects prompt injection, data exfiltration, and scope violations in real time.
