How is DeepInspect different from Noma Security?

Noma Security discovers AI and ML assets across an organization, models, agents, MCP servers, datasets, and notebooks, then scores them for misconfiguration and exposure risk. It operates on a continuous but asynchronous cadence: scan, score, report, remediate. DeepInspect is a stateless proxy that sits inline on every HTTP request between an authenticated user or agent and any LLM, deciding whether to pass or block each request, with redaction available in real time, and producing a signed audit record for each one. Noma answers what AI exists and how it is configured. DeepInspect answers whether a specific request was authorized, at the moment it happened.

What is AI security posture management (AI-SPM)?

AI-SPM is a product category focused on discovering and assessing the AI and machine learning assets an organization runs: models, training data, notebooks, pipelines, agents, and the infrastructure connecting them. An AI-SPM platform maps what exists, checks configuration against known risks and compliance frameworks, and flags exposure before or as it becomes a live problem. It is a posture and inventory function, closer to cloud security posture management applied to AI, rather than a control that intercepts individual requests as they happen.

Can Noma Security replace DeepInspect?

No, because they operate at different layers. Noma's posture graph can tell a team that an agent has broader data access than it should, but it does not sit in the HTTP path between a caller and an LLM, so it has no mechanism to block or redact a specific request in flight. If a compliance team needs a signed record proving who was allowed to send a specific prompt to a specific model at a specific time, that record has to come from something evaluating the request itself, not from a periodic asset scan.

Can DeepInspect replace Noma Security?

No. DeepInspect enforces identity-based policy on HTTP traffic between authenticated callers and LLMs; it does not scan an organization's cloud and SaaS environment for undiscovered agents, models, or notebooks, and it makes no claim to. A team that needs to know what AI exists across its environment, and whether that AI is configured safely before it is ever called, needs a discovery and posture tool for that job. DeepInspect governs traffic it can see; it does not go looking for assets it was never told about.

Most regulated teams that run agentic AI at any scale end up wanting both, because the two answer questions that show up on different desks. Security and platform teams that own the data science environment want Noma's inventory and posture scoring, so they know what is running and where the configuration risk sits. Compliance and audit teams want DeepInspect's per-request enforcement and signed records, so they can answer a regulator's question about a specific decision months later. Running only one leaves a real gap: posture without enforcement means known risks stay live between scans, and enforcement without posture means you are gating traffic to agents you may not know exist yet.

Does Noma Security enforce policy on live requests?

Noma's core AI-SPM layer is a discovery and assessment function: it identifies assets and scores their configuration, then feeds that context to remediation workflows and compliance mapping. Noma has added separate modules, an agent access control component and a detection-and-response layer, that apply configurable actions such as monitor or alert, then block or mask, to agent sessions and tool calls. Those modules extend the platform toward runtime response for agentic workflows specifically. They are a different mechanism from a universal HTTP proxy that binds identity-based policy to every individual request reaching any LLM endpoint, which is what DeepInspect does.

Does DeepInspect discover shadow AI or unauthorized models across our environment?

No. DeepInspect governs and logs traffic that passes through it as the proxy between an authenticated caller and an LLM. It does not scan cloud accounts and source repositories, plus SaaS platforms, to find models, agents, or notebooks that exist outside that traffic path. An organization that needs to find unregistered agents or shadow deployments before routing them through any enforcement layer needs a discovery tool built for that purpose, which is the problem Noma's AI-SPM product is built to solve.

Which one satisfies EU AI Act Article 12 logging requirements?

Article 12 requires automatic logging of events over the lifetime of a high-risk AI system, capable of enabling ex-post traceability of specific decisions. Noma's compliance mapping shows where an organization's AI assets stand against a framework like the EU AI Act at a point in time, which supports the broader governance obligation but is not itself a per-decision log of live requests. DeepInspect produces a signed audit record for every individual request, including identity and policy version, plus data classification, outcome, and timestamp, which is the kind of per-decision evidence Article 12 is asking for. Most teams need the posture mapping and the per-decision log for a complete answer.

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August 23, 2026

DeepInspect vs Noma Security: Runtime Enforcement vs AI Security Posture Management

Noma Security discovers and assesses AI and ML assets across an organization for misconfiguration and exposure risk, on a continuous but asynchronous cadence. DeepInspect enforces identity-bound policy on every live request between a user or agent and an LLM, in real time, and produces a signed audit record. Teams evaluating a Noma Security alternative for access control need to know these sit at different layers of the same AI estate.

ByParminder Singh· Founder & CEO, DeepInspect Inc.

Comparisons & Alternativesai-securityai-governancellm-securitypolicy-enforcementforensic-auditinline-enforcementshadow-ai

DeepInspect vs Noma Security: Runtime Enforcement vs AI Security Posture Management

A platform security lead I spoke with in June had just finished onboarding Noma Security across their data science environment. The first scan found 340-odd agents and models nobody had a ticket for, plus a dozen MCP servers wired into internal tools with no owner listed. Good find, real risk. Then their compliance team asked a narrower question: when one of those agents called Anthropic's API last Tuesday at 3:14pm with a claimant's medical record sitting in the prompt, who authorized that call, and can you produce the record six months from now. The posture dashboard had no field for that answer, because it was never built to hold one.

That gap is what this piece is about. DeepInspect and Noma Security get compared because both show up in the same AI security procurement conversations, and both touch AI traffic somewhere in the stack. The overlap stops there. Noma tells you what exists in your AI environment and whether it is configured safely. DeepInspect decides, on each individual request, whether that request is allowed, and writes down what it decided.

TL;DR

Noma Security discovers and assesses AI and ML assets for misconfiguration, vulnerability, and exposure risk on a continuous scanning cadence.
DeepInspect sits inline between an authenticated user or agent and any LLM, deciding whether to pass or block each request, with redaction available before the model sees it.
Noma answers what AI is running and whether it is configured safely. DeepInspect answers whether a specific call from a specific caller was allowed, then proves it later.
Noma feeds remediation and compliance mapping. DeepInspect creates a signed audit record for each decision. Regulated teams often run both.

Noma Security

Noma Security, founded in Tel Aviv in 2023 by Niv Braun and Alon Tron, builds what it calls an AI Security Posture Management platform. The core mechanism is discovery: the product scans an organization's cloud, SaaS, and self-hosted environments and builds an inventory of every model, agent, MCP server, tool, dataset, and notebook it can find, through integrations across more than 80 data, AI, and MLOps platforms according to Noma's own AI-SPM product page. Teams typically discover far more agents than they expected, since shadow deployments accumulate the same way shadow AI usage accumulates anywhere else in an organization: a data scientist wires an agent into a Slack workflow, nobody files the change, and it sits there for a year.

Once an asset is found, Noma scores it against hundreds of built-in policies covering things like exposed credentials in a notebook, a model pulled from an unvetted source, or a data pipeline with broader access than it needs, then maps findings to frameworks including NIST AI RMF and the EU AI Act, plus ISO 42001 and the OWASP LLM Top 10. That scoring runs on a continuous cadence rather than a one-time audit, so the inventory stays current as new agents and models get added. Noma has since layered an agent access control module and a detection-and-response component on top of that same posture graph, aimed at agent sessions and tool calls rather than arbitrary LLM API traffic. The foundation of the product, and the reason it gets bought, remains the discovery and posture layer: know what AI exists, know how it is configured, know where the exposure sits.

That is a genuinely different question from whether one specific request, from one specific person, should have been allowed to reach a model five minutes ago.

The inline enforcement proxy

DeepInspect is a stateless proxy that sits between authenticated users or agents and any LLM. Every HTTP request to a model API passes through it before the model sees it, and every response passes through it before the caller sees it.

The evaluation happens per request, not on a scan interval. DeepInspect reads the identity context supplied by the calling application, checks what role or authorization that identity carries, and applies whatever policy decision point governs that combination, then decides whether to pass or block the traffic, with redaction available before it reaches the model. Because the proxy operates at the HTTP layer rather than inside a provider SDK or an agent framework, it works the same way in front of OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, or a self-hosted endpoint. Different layer, different question. This is the same inline enforcement pattern I argued for in 22-Second Breach Windows Mean Your AI Enforcement Must Be Inline. Google Mandiant's M-Trends 2026 report found the median handoff time from initial access to a secondary threat group had dropped to 22 seconds. A posture scan that runs nightly, or even hourly, is not built to intercept something that moves that fast.

Every decision produces a signed, tamper-evident audit record: identity, policy version, data classification, outcome, timestamp. That record commits before the response reaches the calling application, keeping the evidence outside the calling application's own custody. It is the kind of record a regulator or an internal audit team wants handed to them directly, not reconstructed from logs after the fact.

Feature comparison

Noma and DeepInspect rarely compete for the same procurement line item, because they are measuring different things about the same AI estate. The breakdown below lays out where each one actually sits.

What it measures: Noma measures AI asset configuration and exposure risk across the environment. DeepInspect measures request legitimacy: identity and authorization, plus policy compliance, on each call.
When it acts: Noma runs continuous discovery and posture scoring, largely ahead of or alongside deployment. DeepInspect evaluates before each request reaches the model, in the request path itself.
Enforcement capability: Noma's posture layer surfaces risk findings and compliance gaps for a team to remediate. DeepInspect makes an inline pass or block decision on every request, with redaction available before any damage is possible.
Identity awareness: Noma treats identity as one risk signal among many in its posture graph: who has access to which agent or tool. DeepInspect binds every enforcement decision to the authenticated caller's identity and role at the moment of the request.
Audit output: Noma produces posture reports mapped to compliance frameworks like NIST AI RMF and the EU AI Act. DeepInspect produces a signed, per-decision audit record built for regulatory disclosure.
Asset scope: Noma inventories the AI and ML development pipeline itself: models, datasets, notebooks, MCP servers, agents. DeepInspect operates strictly on HTTP traffic between authenticated callers and LLM endpoints; it does not scan the pipeline that produced the model.
Integration pattern: Noma deploys through integrations across cloud and SaaS platforms, plus source-control integrations, with no code changes required. DeepInspect integrates as a proxy in the request path, independent of the calling application's own instrumentation.
Primary buyer: Noma is typically bought by AI/ML platform and security teams that own the data science and MLOps environment. DeepInspect is typically bought by security and compliance teams, plus platform teams, that own access control and audit obligations for live traffic.
Failure mode on error: Noma flags a misconfigured or exposed asset for remediation; the asset keeps running until someone fixes it. DeepInspect can fail closed, denying a request by default when policy evaluation is ambiguous or unavailable.

Pick Noma Security if...

Noma fits teams whose open question is still about what AI exists in their environment, not about who was allowed to call it.

Your priority is finding every model and agent, plus MCP servers and notebooks, running across your organization, including the ones nobody filed a change ticket for.
You need to score AI/ML asset configuration against frameworks like NIST AI RMF or ISO 42001 before or as new agents get deployed.
You want a continuously updated inventory of your AI supply chain so your team can prioritize which exposed dataset or misconfigured pipeline to fix first.
Your open question is "what AI is running here and is it configured safely," not "who was allowed to make this specific call."

Pick DeepInspect if...

DeepInspect fits teams whose question has moved from inventory and configuration to access control and evidence.

You need per-request, identity-aware enforcement, with a real pass or block decision made on live traffic, with redaction available rather than a periodic risk score.
A regulator or auditor asks who accessed a given data set through a model and under which policy, with proof available, and last month's posture scan will not answer that.
You are working toward the EU AI Act's Article 12 logging deadline, which took effect on August 2, 2026, and your evidence needs to come from records the calling application does not control.
You need enforcement that behaves the same way in front of any LLM endpoint, regardless of whether that endpoint has already been catalogued by a discovery scan.

Teams that end up running both rarely plan it that way from the start. The posture side gets funded first, because a CISO needs an inventory before they can defend a budget line for anything else. Enforcement gets funded once someone in compliance or legal asks the question a discovery graph was never built to answer. Watching a solid Noma rollout stall in an audit review over a question about one specific request is an avoidable way to lose a quarter, and a mildly frustrating one to watch happen twice.

DeepInspect

This is the access-control gap Noma's posture graph was never built to close. Noma's discovery scan tells you an agent exists, what it can reach, and whether its configuration looks risky. It does not tell you whether the person or agent behind a specific call was authorized to send a claimant's medical record into that prompt, and a posture dashboard has no mechanism to redact that data before it reaches the model.

DeepInspect sits at the AI request boundary as a model-agnostic proxy, checking identity and role, plus data classification, on every request and producing a signed audit record for every decision it makes. It runs alongside posture tooling rather than replacing it: Noma tells you what exists and how it is configured, DeepInspect tells you who was allowed to use it on a given call and proves the answer later.

If you are facing the August deadline, let's talk.

Frequently asked questions

How is DeepInspect different from Noma Security?: Noma Security discovers AI and ML assets across an organization, models, agents, MCP servers, datasets, and notebooks, then scores them for misconfiguration and exposure risk. It operates on a continuous but asynchronous cadence: scan, score, report, remediate. DeepInspect is a stateless proxy that sits inline on every HTTP request between an authenticated user or agent and any LLM, deciding whether to pass or block each request, with redaction available in real time, and producing a signed audit record for each one. Noma answers what AI exists and how it is configured. DeepInspect answers whether a specific request was authorized, at the moment it happened.
What is AI security posture management (AI-SPM)?: AI-SPM is a product category focused on discovering and assessing the AI and machine learning assets an organization runs: models, training data, notebooks, pipelines, agents, and the infrastructure connecting them. An AI-SPM platform maps what exists, checks configuration against known risks and compliance frameworks, and flags exposure before or as it becomes a live problem. It is a posture and inventory function, closer to cloud security posture management applied to AI, rather than a control that intercepts individual requests as they happen.
Can Noma Security replace DeepInspect?: No, because they operate at different layers. Noma's posture graph can tell a team that an agent has broader data access than it should, but it does not sit in the HTTP path between a caller and an LLM, so it has no mechanism to block or redact a specific request in flight. If a compliance team needs a signed record proving who was allowed to send a specific prompt to a specific model at a specific time, that record has to come from something evaluating the request itself, not from a periodic asset scan.
Can DeepInspect replace Noma Security?: No. DeepInspect enforces identity-based policy on HTTP traffic between authenticated callers and LLMs; it does not scan an organization's cloud and SaaS environment for undiscovered agents, models, or notebooks, and it makes no claim to. A team that needs to know what AI exists across its environment, and whether that AI is configured safely before it is ever called, needs a discovery and posture tool for that job. DeepInspect governs traffic it can see; it does not go looking for assets it was never told about.
Do we need both?: Most regulated teams that run agentic AI at any scale end up wanting both, because the two answer questions that show up on different desks. Security and platform teams that own the data science environment want Noma's inventory and posture scoring, so they know what is running and where the configuration risk sits. Compliance and audit teams want DeepInspect's per-request enforcement and signed records, so they can answer a regulator's question about a specific decision months later. Running only one leaves a real gap: posture without enforcement means known risks stay live between scans, and enforcement without posture means you are gating traffic to agents you may not know exist yet.
Does Noma Security enforce policy on live requests?: Noma's core AI-SPM layer is a discovery and assessment function: it identifies assets and scores their configuration, then feeds that context to remediation workflows and compliance mapping. Noma has added separate modules, an agent access control component and a detection-and-response layer, that apply configurable actions such as monitor or alert, then block or mask, to agent sessions and tool calls. Those modules extend the platform toward runtime response for agentic workflows specifically. They are a different mechanism from a universal HTTP proxy that binds identity-based policy to every individual request reaching any LLM endpoint, which is what DeepInspect does.
Does DeepInspect discover shadow AI or unauthorized models across our environment?: No. DeepInspect governs and logs traffic that passes through it as the proxy between an authenticated caller and an LLM. It does not scan cloud accounts and source repositories, plus SaaS platforms, to find models, agents, or notebooks that exist outside that traffic path. An organization that needs to find unregistered agents or shadow deployments before routing them through any enforcement layer needs a discovery tool built for that purpose, which is the problem Noma's AI-SPM product is built to solve.
Which one satisfies EU AI Act Article 12 logging requirements?: Article 12 requires automatic logging of events over the lifetime of a high-risk AI system, capable of enabling ex-post traceability of specific decisions. Noma's compliance mapping shows where an organization's AI assets stand against a framework like the EU AI Act at a point in time, which supports the broader governance obligation but is not itself a per-decision log of live requests. DeepInspect produces a signed audit record for every individual request, including identity and policy version, plus data classification, outcome, and timestamp, which is the kind of per-decision evidence Article 12 is asking for. Most teams need the posture mapping and the per-decision log for a complete answer.

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