How do Redline AI, Noma and Obsidian compare for securing AI agents at a growing SaaS company?

Redline AI, Noma and Obsidian, side by side

Redline facts in this comparison are from the Redline product pages. Noma facts are from noma.security, and Obsidian facts are from obsidiansecurity.com. The vendor pages were read on 15 September 2026. Where a cell says that a page does not describe something, that is the scope of the comparison rather than a claim about what the vendor may do elsewhere.

The table shows why a product-centred comparison can be misleading. Redline is concerned with the assembled behaviour of an agent that the engineering team controls. Noma combines inventory, adversarial testing and runtime policy across a wider AI estate. Obsidian places particular emphasis on enterprise agent discovery, identity, permissions and execution-layer controls.

The meaningful question is therefore not which product has the longest feature list. It is which system has the right visibility and enforcement point for the agents creating risk in the organisation.

What does each platform discover?

Redline begins with an explicit connection from the agent to its SDK. The documented integration covers agents built with LangChain and LangGraph, CrewAI, Pydantic AI, the OpenAI Agents SDK, Agno, the Vercel AI SDK or a team’s own loop. This makes the boundary clear: Redline covers the agents connected to it through the SDK.

Noma describes discovery across cloud, SaaS and developer environments. Its documented coverage includes Bedrock, Azure AI Foundry, Databricks, LangChain and CrewAI code, as well as Copilot Studio and Agentforce. It also describes Claude Code and Cursor through hooks. Its inventory includes agents, models, MCP servers and skills.

Obsidian describes discovery of every agent across the enterprise, with the agent mapped to its owner, connected applications, MCP servers and models. Its documented platforms include Amazon Bedrock, Azure Foundry, Copilot Studio, ChatGPT, Google Vertex, Salesforce Agentforce and n8n.

For a growing SaaS company, this distinction separates product security from internal AI governance. If the concern is an agent shipped as part of the company’s product, the relevant discovery mechanism is the SDK connection. If the concern is what employees have assembled across SaaS applications and developer environments, Noma and Obsidian document broader discovery paths.

Which platform tests an agent before release?

Redline and Noma both describe adversarial testing before release, but they test from different perspectives.

Redline attacks the assembled agent where it runs, with its real tools, MCP servers and skills attached. It uses 11,204 cases across 16 families, with one container per run. A run passes only when the agent both performs the intended job and refuses the attack. A second agent inspects the machine and decides the result from evidence.

This matters when tool access and surrounding components are part of the security question. Testing the model or prompt in isolation does not represent the assembled system described by Redline. The test concerns the agent’s behaviour with the attachments that can affect its operation.

Noma’s AI Red Teaming runs an adversarial LLM against agents and applications in multi-turn sequences. The campaigns escalate at each turn and combine techniques including prompt injection, jailbreak, data leakage and denial-of-wallet. Noma maps the activity to OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, EU AI Act and ISO 42001. Its findings feed Noma’s runtime policies.

Obsidian’s agent security page describes continuous risk scoring against OWASP risk factors whenever an agent is published or modified. The page does not describe attack testing. That gives Obsidian a documented approach to risk scoring around agent changes, but the page does not describe red-team campaigns.

Engineering teams should therefore distinguish between:

• Testing the assembled agent with its real tools and connected components.
• Running multi-turn adversarial campaigns against agents and applications.
• Scoring an agent’s risk when it is published or modified.

These approaches address related but different questions. The first asks whether a specific implementation can complete its job without accepting attacks. The second examines how an agent behaves under a compound adversarial sequence. The third provides a risk view associated with changes to an agent.

For a practical Redline workflow, see how to test an AI agent before you ship it.

Where do runtime controls operate?

Runtime enforcement is one of the clearest differences between the products.

Noma lists agent hooks, MCP gateways, AI gateways, agent SDKs and direct APIs as enforcement points. A gateway is therefore one option in its documented model, not a requirement. Noma applies policy to the whole session, including prompts, responses, tool calls and responses, skills, other agents, identity, data in reach and baseline behaviour. Its documented runtime verdicts are alert, block, mask data or route to a human.

Obsidian describes guardrails at the execution layer, firing before the action executes. Its runtime view includes behavioural baselines across users and agents, along with the permissions each agent actually uses. It can block unapproved actions, with policies applied per agent, per team or across the organisation.

Redline runs its rules inside the agent process. Because the control is placed where the tool call is formed, a rule sees every argument and can compare it with the session. Redline evaluates the tool call and every argument, scores each incoming message with a classifier, and records calls to honeypot tools. Its verdicts are deny, require approval, warn or log.

The architectural difference affects implementation decisions:

• Teams using Noma can select among the enforcement points it documents, according to where the agent and its interfaces are exposed.
• Teams using Obsidian receive execution-layer guardrails, with permissions and behavioural baselines forming part of the documented control model.
• Teams using Redline place the policy in the agent process, without putting a proxy in front of traffic.

Redline’s approach is described in more detail in runtime guardrails for AI agents.

How do the products handle prompt injection and identity?

Prompt-injection coverage and identity coverage are separate evaluation areas.

Redline documents Redline Guard v5 with 0.9484 recall at 1.4% false positives on BIPIA, with its losses published. It scores each incoming message with a classifier. Honeypot tools provide another signal: if a hijacked agent calls one, that call becomes a recorded fact for the runtime policy and monitoring flow.

Noma documents prompt-injection detection, including indirect injection through tool responses. Noma states a 2–3x improvement over native guardrails in its own proof-of-concept benchmarks. Its runtime model also evaluates the whole session, including identity, data in reach and baseline behaviour.

Obsidian’s agent security page does not describe prompt-injection detection. It does describe identity and permissions. The platform maps each SaaS agent to the person who built it, right-sizes permissions to actual use, flags embedded credentials, and resolves both the agent’s author and the identity under which it runs.

Redline does not cover agents’ permissions inside third-party SaaS. This is a material boundary, not a minor difference in terminology. Redline’s documented focus is the agent process and the behaviour produced by the connected implementation. Noma and Obsidian document controls that extend into SaaS-agent identity and access.

A buyer should keep these questions separate:

• Can the system detect or respond to prompt injection?
• Can it identify who created an agent?
• Can it determine which identity the agent runs as?
• Can it reduce permissions inside third-party SaaS?
• Can it observe the actions and arguments produced by the agent process?

The three products answer these questions differently. No single row in the comparison should be treated as a substitute for the others.

Which platform fits a growing SaaS company?

The best fit depends on where the organisation’s important agents exist and what security needs to happen first.

Choose Obsidian when SaaS identity is the starting point

Obsidian is the closest fit when the exposure is agents and copilots inside the SaaS applications the company runs, including Salesforce, Copilot Studio and ChatGPT. Its documented starting point is enterprise-wide agent discovery, ownership mapping, connected applications, MCP servers and models.

Its documented permission controls are also central: it right-sizes permissions to actual use, flags embedded credentials, and resolves the author and runtime identity. This makes the product relevant when the first task is understanding what an agent can reach and reducing that access.

Choose Noma when the organisation needs an AI estate view

Noma is the closer fit when security must govern homegrown, SaaS and endpoint agents, alongside models and MCP servers. Its pages describe discovery, AI Red Teaming and runtime policy in one platform.

That breadth is reflected in its documented control points and evaluation scope. Noma can enforce through agent hooks, MCP gateways, AI gateways, agent SDKs and direct APIs, while judging actions in the context of the whole session. Its testing model also combines multi-turn attack campaigns with compliance mappings and runtime policy.

Choose Redline when the product ships the agents

Redline is the closer fit when the agents that matter most are the ones the company’s product ships to customers. Its SDK connects to the agent your team builds, tests the assembled implementation with its real tools attached, grades each run on evidence, and applies runtime rules from inside the process.

This creates a release-to-production path for the same class of agent: attack it before release, then enforce and observe the agent in production from inside the process. The limitation is equally clear: Redline does not cover agents’ permissions inside third-party SaaS.

A growing SaaS company may have both kinds of exposure. Its product may ship agents while employees also assemble agents in Salesforce, Copilot Studio, ChatGPT or other documented platforms. Using more than one product is reasonable where the jobs are distinct: governing agents inside third-party SaaS and testing agents in the company’s own product occur at different points.

A practical evaluation framework

Start by identifying the agent population that creates the immediate risk. If the population is unknown because employees can create agents across SaaS and developer environments, discovery and ownership should be the first evaluation area. If the population is known and consists of agents built by the product team, test coverage and runtime placement should come first.

Then compare the enforcement model with the architecture already in use. Noma documents multiple control points. Obsidian documents execution-layer guardrails. Redline documents controls inside the agent process. The relevant choice is where the organisation can reliably observe the action before it has an unwanted effect.

Next, define what constitutes evidence. Redline’s documented pass decision requires the agent to perform its job and refuse the attack, with a second agent inspecting the machine. Noma’s campaigns produce findings that feed runtime policies. Obsidian documents continuous risk scoring against OWASP risk factors when an agent is published or modified. These are different forms of evidence and should not be treated as interchangeable.

Finally, assess identity separately from agent behaviour. A team may need to know who built a SaaS agent and which identity it runs as, while also needing to know whether a product agent accepted an indirect instruction through a tool response. Noma and Obsidian document SaaS identity coverage; Redline documents process-level controls and prompt-injection detection.

Key takeaways

• Redline starts with agents the engineering team builds, connecting through its SDK and testing the assembled implementation with its real tools, MCP servers and skills.
• Noma starts with the wider AI estate, covering discovery, multi-turn AI Red Teaming and runtime policy across documented hooks, gateways, SDKs and APIs.
• Obsidian starts with enterprise agent discovery, SaaS identity, permissions and execution-layer guardrails.
• Redline documents 11,204 attack cases across 16 families, while Noma documents multi-turn campaigns and Obsidian documents continuous OWASP risk scoring.
• Redline does not cover agents’ permissions inside third-party SaaS; Noma and Obsidian document SaaS identity and permission capabilities.
• A growing SaaS company may use more than one approach when it needs both product-agent testing and governance of agents employees assemble in SaaS platforms.

Common questions

Which of the three tests AI agents before release?

Redline and Noma. Redline attacks the assembled agent with its real tools attached and grades each run on evidence. Noma’s AI Red Teaming runs multi-turn adversarial campaigns. Obsidian’s agent security page describes continuous risk scoring against OWASP risk factors rather than attack testing.

Do Noma or Obsidian need a proxy in front of the agent?

Noma lists several enforcement points: agent hooks, MCP gateways, AI gateways, agent SDKs and direct APIs. A gateway is therefore one option, not a requirement. Obsidian describes guardrails at the execution layer. Redline puts nothing in front of traffic because its rules run inside the agent process.

Does Redline discover agents employees build in SaaS platforms?

No. Redline covers agents connected to it through its SDK. Discovering Copilot Studio, Agentforce or ChatGPT agents that nobody connected is what Noma and Obsidian both document.

Can a SaaS company use more than one?

Yes. The products overlap less than the category name suggests. Governing agents inside third-party SaaS and testing the agents in a company’s own product are different jobs, performed at different points. A company with both exposure types can evaluate each control against the agent population it is intended to cover.

Sources

• Noma Security — discovery, policy, continuous testing, runtime detection and Open Enforcement control points.
• Homegrown agents and SaaS agents — platforms covered, AI-DR verdicts, the proof-of-concept detection claim and maker identity.
• AI Red Teaming and Agent behaviour runtime control — multi-turn campaigns, compliance presets and session-level enforcement.
• Obsidian AI Agent Security — discovery platforms, least privilege, OWASP risk scoring, execution-layer guardrails and audit evidence.
• Redline: Guard benchmark, monitoring docs, Redline AI vs Akto and Redline AI vs Prisma AIRS.