About
Noma Security is a comprehensive AI security platform built to address the unique risks introduced by modern AI systems — including LLMs, RAG pipelines, AI agents, MCP servers, and coding assistants. As AI adoption accelerates in the enterprise, so do attack surfaces: prompt injection, model poisoning, jailbreaks, data leakage, and agent misconfigurations are threats that traditional security tools simply cannot detect. Noma's platform operates across three core pillars: Discover, Secure, and Protect. The Discovery layer provides full visibility into every model, agent, MCP server, data source, and their dependency chains across the enterprise. The Secure layer helps teams build security in from the ground up — defining approved AI supply chains, setting identity and access controls, governing autonomous agent behavior, and validating AI through continuous red teaming and prompt injection testing. The Protect layer enforces real-time runtime policies across every prompt, response, and tool call — catching threats like prompt injection and jailbreaks while preventing sensitive data from leaking out of the environment. Noma seamlessly integrates into existing SecOps workflows and supports compliance with evolving AI regulations. It is purpose-built for security teams, developers, and risk professionals at enterprises that are deploying AI at scale and need to stay ahead of tomorrow's threats.
Key Features
- AI Security Posture Management: Continuously discovers and maps every model, agent, MCP server, and data source across the enterprise to identify misconfigurations and vulnerabilities before they can be exploited.
- Red Teaming & Offensive Testing: Validates AI systems through continuous adversarial testing for prompt injection, jailbreaks, and data leakage to expose weaknesses before attackers do.
- Real-Time Runtime Protection: Monitors and enforces security, privacy, and compliance policies on every prompt, response, and tool call as AI operates in production.
- MCP Server Security: Provides real-time threat detection and policy enforcement across all Model Context Protocol (MCP) servers used by AI agents.
- AI Governance & Compliance: Built-in compliance controls and real-time monitoring ensure AI behavior meets regulatory requirements before autonomous agents take action.
Use Cases
- Securing enterprise AI agent deployments by monitoring every tool call, prompt, and response in real time to prevent prompt injection and data exfiltration.
- Achieving compliance with AI regulations (e.g., EU AI Act, NIST AI RMF) through built-in governance controls and automated policy enforcement.
- Mapping and governing the AI supply chain by identifying all approved models, MCP servers, and third-party AI tools used across the organization.
- Conducting red team testing of internal LLM applications to discover jailbreak vulnerabilities and data leakage risks before production deployment.
- Providing security operations teams with full visibility into AI infrastructure to detect misconfigurations, shadow AI usage, and agentic behavior anomalies.
Pros
- End-to-End AI Coverage: Covers the full AI stack — models, agents, RAG pipelines, MCP servers, and SaaS AI tools — in a single unified platform, eliminating security blind spots.
- Seamless SecOps Integration: Designed to integrate into existing security operations workflows without disrupting business, development, or risk teams, enabling fast time-to-value.
- Purpose-Built for AI-Specific Threats: Detects and prevents AI-native attack vectors like prompt injection, model poisoning, and jailbreaks that traditional security tools cannot handle.
- Proactively Evolving Defenses: Rapidly innovating platform that adapts defenses in real time as new AI threats and models emerge, keeping enterprises ahead of the threat curve.
Cons
- Enterprise-Only Pricing: Noma is positioned as an enterprise solution with demo-based sales, making it inaccessible or cost-prohibitive for smaller teams or startups.
- Requires Broad AI Inventory: To maximize value, organizations need to have a well-defined and catalogued AI landscape — teams with fragmented or undocumented AI deployments may face onboarding friction.
- Specialized Use Case: Noma is purpose-built for AI security and governance; it does not replace broader cybersecurity platforms and requires complementary general security tooling.
Frequently Asked Questions
What types of AI systems does Noma Security protect?
Noma protects a wide range of AI systems including homegrown LLM applications, RAG pipelines, autonomous AI agents, SaaS AI tools, coding assistants, and MCP servers. It covers both internally built and third-party AI components across the enterprise.
How does Noma detect prompt injection and jailbreak attacks?
Noma monitors every prompt and response at runtime, applying security policies that detect manipulation attempts in real time. Its red teaming module also proactively tests AI systems for prompt injection and jailbreak vulnerabilities before deployment.
Does Noma integrate with existing security operations workflows?
Yes. Noma is built as a contextual, integrated platform designed to fit into existing SecOps processes, enabling security teams to gain AI visibility and protection without disrupting their current tooling or workflows.
How does Noma help with AI regulatory compliance?
Noma includes built-in compliance controls and real-time monitoring dashboards that track AI behavior against evolving regulatory frameworks. Compliance policies can be enforced at runtime to ensure AI agents meet requirements before taking any action.
What is AI Security Posture Management (AI-SPM)?
AI-SPM is Noma's capability to continuously discover, inventory, and assess the security posture of all AI assets in an organization — including models, agents, data sources, and their interconnections — so security teams can identify and remediate risks proactively.
