Noma Security is an agentic AI security platform whose public positioning centers on AI discovery, agentic risk mapping, posture management, automated red-teaming, and runtime protection — work recognized by Gartner, which named Noma a Cool Vendor in AI Security for 2025. Teams researching alternatives are usually choosing between agent-native pure-play vendors like Noma, platform-bundled options that fold AI security into a broader security suite, and governance layers that add identity and runtime enforcement on top of testing. This post covers Noma's public positioning, its nearest peers, and how to evaluate the options.
What Noma Security's public positioning covers
Per Gartner's Cool Vendor recognition and Noma's own materials, the platform spans four areas: discovering AI usage and agents across an estate, mapping agentic risk (what an agent can reach and what it could do if compromised), assessing security posture, and running automated red-teaming and runtime protection. That combination — discovery plus adversarial testing plus in-production defense — is a distinct emphasis from vendors that focus on just one slice, such as pure detection APIs or pure posture scanning.
Noma's research arm, Noma Labs, backs this positioning with public vulnerability research. In September 2025, Noma Labs disclosed ForcedLeak, a CVSS 9.4 indirect prompt-injection chain in Salesforce Agentforce that let an external attacker exfiltrate CRM data through a chain of trusted-looking inputs; Salesforce's fix blocked output to untrusted URLs. Disclosures like this are a reasonable proxy for a vendor's testing depth, and they are the kind of fact this post treats as verifiable rather than promotional — it is public, dated, and independently reported.
Noma vs WitnessAI: two different centers of gravity
Noma and WitnessAI are frequently compared as adjacent agentic-AI-security vendors, and industry write-ups draw a consistent line between them: Noma's public emphasis sits closer to testing, red-teaming, and agent containment, while WitnessAI's public emphasis sits closer to compliance and audit workflows for AI access. Neither framing implies the other vendor lacks capability outside its emphasis — it describes where each vendor's public marketing and case studies concentrate. If your buying trigger is "we need to stress-test our agents before they ship," that points toward Noma's stated strength. If it is "we need defensible evidence of what our AI systems accessed and why," that points toward the audit-forward positioning covered in our WitnessAI alternatives piece.
The category split: agent-native pure-plays vs platform-bundled
Industry coverage of the agentic AI security market consistently draws a line between two groups. Agent-native pure-plays — Noma, Pillar, Lasso, and Prompt Security among them — build specifically for autonomous-agent risk: goal hijacking, tool misuse, memory poisoning, and agent-to-agent abuse. Platform-bundled options — HiddenLayer, CrowdStrike Falcon AIDR, Protect AI (now part of Palo Alto Networks), and Lakera — extend an existing detection-and-response or security-platform footprint to cover AI and agents as one more asset class. Neither structure is objectively better; the deciding factor documented across these comparisons is whether the buyer is already standardized on the platform vendor. If you already run Falcon or a Palo Alto stack, the bundled option inherits your existing operational model. If you are building agent security as a first-class program, the pure-play vendors are purpose-built for that shape of problem. We cover this decision in more depth in platform-bundled vs pure-play AI agent security.
Peer vendors in the agent-native cluster
Pillar, Lasso, and Prompt Security are named alongside Noma in industry roundups of agent-native pure-plays. Prompt Security in particular positions around enterprise GenAI security spanning employee AI usage, applications, and agent and MCP traffic, per its public materials; a fuller treatment of that vendor, including its current ownership, sits in our Prompt Security alternatives post. Pillar and Lasso are consistently named in the same comparison sets as agent-native options but have thinner independent public documentation than Noma or Prompt Security at the time of writing — worth direct due diligence with each vendor before shortlisting.
Platform-bundled alternatives
HiddenLayer, CrowdStrike Falcon AI Detection and Response, Protect AI (Palo Alto Networks), and Lakera are the platform-bundled names that recur in the same comparison sets as Noma. Each extends a pre-existing security platform's reach into AI and agent workloads rather than starting from an agent-native architecture. Lakera in particular has a narrower public focus — AI-native content defense, prompt-injection and jailbreak detection via API — that we cover separately in Lakera alternatives for LLM guardrails, since it sits closer to the guardrails category than to full agentic risk mapping.
Comparing the shapes of coverage
| Dimension | Agent-native pure-play (Noma, Pillar, Lasso) | Platform-bundled (HiddenLayer, Falcon AIDR, Protect AI, Lakera) |
|---|---|---|
| Primary buying trigger | Agent-specific risk is the program, not a feature | AI/agent risk extends an existing platform relationship |
| Depth on agent-specific threats | Purpose-built: goal hijacking, tool misuse, agent containment | Extends detection-and-response patterns to AI as an asset class |
| Integration cost | New vendor relationship, new tooling | Inherits existing platform's operational model |
| Best fit | Agent security as a standalone program | Already standardized on the platform vendor |
Use this as a starting lens, not a scorecard — the right column for your organization depends on what you already run and how quickly your agent estate is growing.
What a testing-and-containment vendor does not replace
Automated red-teaming and runtime protection answer "can this agent be broken, and did we catch it in production." They generally do not answer "does every agent have its own verifiable identity," "what exactly can this agent do right now, and who authorized it," or "can I produce an audit trail that holds up to a regulator or auditor." Those are identity, authorization, and evidence questions, and they sit closer to the governance-control-plane category than to testing. Praesidia is one option in that adjacent category — an AI agent security and governance control plane covering agent identity and access, guardrails, audit evidence, and cost controls in one place — and teams sometimes pair a testing-focused vendor like Noma with a governance layer rather than choosing one over the other. See runtime security vs security posture for the underlying distinction, and our AI agent security guide for the fuller category map.
Questions to ask during evaluation
A short, concrete checklist travels better than a feature list, because it forces the vendor conversation onto your actual estate rather than their roadmap slide:
- Which agent frameworks and orchestration layers do you test against today — and does that list include what we actually run, not just what is on your roadmap?
- What does a red-team finding look like end to end — a report you read, or an integration that feeds a ticketing or SIEM workflow you already use?
- How does runtime protection behave when it is wrong — does a false positive block a production agent, and how fast can an operator override it?
- Does the platform cover MCP tool-call traffic specifically, given how much 2026 agent risk research centers on tool use rather than chat — see our MCP server security checklist for the surface this needs to reach.
- What happens after the finding — does the vendor stop at detection, or does it also enforce identity, scoping, or containment on the agent in question?
None of these questions has a universally right answer; they exist to surface where a vendor's public positioning and your actual requirements diverge before a contract, not after.
Choosing between them
Start from what triggered the search. If a red-team finding, a near-miss, or a new agent launch pushed you to look for adversarial testing and containment, evaluate the agent-native pure-plays directly against your own agent surface — request a proof-of-concept against representative tool calls, not a generic demo. If your organization already has a detection-and-response platform you trust operationally, evaluate whether that vendor's AI extension covers the specific agent behaviors you are worried about before adding a new vendor relationship. And if the real gap is upstream of testing — no per-agent identity, no enforceable authorization, no audit evidence — a testing vendor alone will not close it regardless of which one you pick.
Common questions
What is Noma Security known for? Per Gartner's 2025 Cool Vendor recognition and Noma's public materials: AI discovery, agentic risk mapping, posture management, automated red-teaming, and runtime protection, backed by public vulnerability research such as the September 2025 ForcedLeak disclosure in Salesforce Agentforce.
How is Noma different from WitnessAI? Public comparisons position Noma closer to testing, red-teaming, and agent containment, and WitnessAI closer to compliance and audit workflows for AI access. Both operate in the same broad agentic-AI-security category; the difference is emphasis, not one vendor lacking what the other has.
Should I choose an agent-native pure-play or a platform-bundled option? It depends on your starting point. Teams building agent security as a dedicated program tend to prefer pure-plays purpose-built for agent risk; teams already standardized on a security platform vendor often get more value extending that relationship, provided the extension actually covers agent-specific behaviors.
Does a red-teaming and runtime-protection vendor replace an identity or governance layer? No — testing and runtime protection tell you whether an agent can be broken and whether an attack was caught. They do not generally provide per-agent identity, continuous authorization, or the audit evidence a compliance review or regulator will ask for; most mature stacks run both layers.
Should I run a testing vendor and a governance layer together, or pick one? Most agent-heavy programs eventually run both, because they answer different questions: a testing and containment vendor tells you whether an agent can be broken; a governance layer tells you who that agent is, what it is authorized to do, and whether you can prove it after the fact. Sequencing depends on which gap is more urgent for your organization right now.