Aurascape and Noma Security both call themselves AI-native security platforms, and from a distance the categories blur together. They do not solve the same problem. Noma is built around securing the AI an organization builds and runs: models, applications, agents, Model Context Protocol (MCP) servers, data pipelines, automated red teaming, and runtime protection. Aurascape is built around governing how the workforce and its agents use AI across every interaction surface, from a browser prompt to a signed agent tool call. The decision is not which platform ships more features. It is which AI risk you have to close first, and for most enterprises the larger uncontrolled risk sits on the usage side.

Short answer: Noma Security fits when the priority is securing the AI applications, models, agents, MCP servers, and data pipelines an enterprise builds or operates. Aurascape fits when the priority is enterprise AI Usage Control across employees, copilots, coding assistants, Embedded SaaS AI, personal and enterprise tenants, local agents, MCP-connected workflows, and inline policy enforcement. If you need one platform for workforce AI usage governance plus governed AI development, Aurascape is the stronger fit, with usage governance as its core strength and build-side coverage through See, Test, Protect.

Last updated: June 24, 2026.

AI Security Split Into Build-Side and Usage-Side Categories

AI security divided along a single fault line: the AI an organization builds versus the AI its workforce and agents use. That split is what separates Aurascape and Noma Security, even though both are AI-native and both govern agents. Their centers of gravity point in opposite directions, and a buyer who treats them as interchangeable will close the wrong risk first.

Noma anchors to the AI lifecycle: discovery and posture for models, pipelines, and homegrown agents, automated red teaming before launch, and runtime protection in production. Aurascape anchors to AI usage: the Commercial AI, Embedded AI, and AI copilots employees touch in a browser, a desktop client, or a command-line interface (CLI), plus the agents and tool calls those interactions trigger. Cisco’s 2025 AI Readiness Index, a survey of more than 8,000 senior IT and business leaders, found 83% of organizations plan to deploy AI agents while only 31% feel equipped to control and secure them (Cisco, 2025).

The usage side is where the adoption-versus-control gap is widest. Gartner predicts that through 2026, at least 80% of unauthorized AI transactions will stem from internal violations of enterprise policy rather than malicious attacks (Gartner, 2025). That is a usage-governance problem, not a build-pipeline problem. For many teams the answer is both sides, but the larger blind spot is the AI employees and agents already use across browsers, SaaS, IDEs, CLIs, personal accounts, enterprise tenants, and MCP-connected workflows.

The AI Security Market Sorts Into Four Vendor Categories

The 2026 AI security market clusters into four categories: AI security posture management for built systems, AI usage governance for workforce interactions, agent and MCP execution control, and the legacy SSE/DLP stack retrofitted for AI. Noma sits in the first, Aurascape in the second and third, and the incumbent network vendors in the fourth. Knowing which category a tool was designed for tells you which risk it actually closes.

This matters because the categories solve genuinely different problems. A posture tool inventories the models and pipelines you ship; a usage-governance tool inspects the prompts and responses your workforce sends; an execution-control layer governs what an agent’s tool calls are allowed to do. A retrofitted SSE or DLP product sees traffic to an AI destination but not the prompt, the response, the tenant, or the tool call inside it.

The market is also growing fast enough that the category choice compounds. Grand View Research sizes the AI trust, risk, and security management market at $2.34B in 2024, projected to reach $7.44B by 2030 at a 21.6% compound annual growth rate (Grand View Research, 2025). Picking a category that maps to your actual blind spot now avoids a re-platform later.

Why AI Security Became Its Own Category Legacy Tools Cannot Cover

Legacy SSE, SASE, CASB, and DLP tools govern destinations and URLs, not the prompts, responses, tenants, and tool calls that carry AI risk. That architectural gap is why AI security became its own category rather than a feature inside the existing network stack. A firewall that sees traffic to an AI service confirms a connection happened; it does not see what the user asked, what the model returned, or whether an agent then executed a tool call against a sensitive system.

The exposure this leaves open is measurable. IBM’s Cost of a Data Breach 2025 report found that 1 in 5 breached organizations reported a breach tied to shadow AI, which added about $670,000 to the average breach (IBM, 2025). Destination-based controls miss shadow AI by design, because the data leaves inside a prompt the tool never decoded.

Aurascape and Noma exist because AI risk lives at the interaction layer, below the URL and above the network packet. Aurascape reads that layer for workforce and agent AI use; Noma reads it for the models and pipelines an organization builds. Neither is a retrofit of a destination-based product, which is the line that separates AI-native platforms from SSE/DLP suites adding an AI dashboard.

Noma Security Centers on the AI Lifecycle You Build and Run

Noma Security fits teams whose primary AI security risk lives in the build-to-runtime lifecycle. Its public platform spans AI Security Posture Management (AI-SPM), automated red teaming, runtime protection, and agentic access control across models, agents, MCP servers, tools, data sources, and AI applications. That makes Noma relevant for application security, AI engineering, platform, and security teams securing the systems they build and operate.

Noma’s runtime layer is enforcement, not only observation. Its public materials describe monitoring prompts, responses, tool calls, MCP interactions, and agent behavior, then alerting, masking, or blocking at the point of execution, with audit trails and blocked MCP connections. Its agentic access control assigns each agent an identity, maintains a registry of agents, MCP servers, and tools, and sets each connection to approved, requires-review, or blocked across the AI environments Noma connects to.

Noma publishes a dedicated automated red-teaming product and posture management for the model and data-pipeline supply chain, including notebook and training-data scanning. Aurascape supports governed AI development, but Noma has strong standalone AI red teaming, model posture, training-data, notebook, and MLOps pipeline security. The rest of this comparison focuses on the governance problem that sits outside the build lifecycle: how employees, agents, copilots, tenants, and tools use AI across the enterprise.

Aurascape Centers on the AI Your Workforce and Agents Already Use

Aurascape starts from the AI interaction itself, which matters when the risk is the AI your workforce already uses, not only the AI your developers ship. It enforces inline across the endpoint, network, and API planes, so it sees AI use whether or not an application was instrumented. Governing that surface takes five capabilities working together, and Aurascape catalogs more than 20,000 AI apps and agents with a 48-hour connector SLA for newly launched tools (Aurascape, 2026).

1. Discover the long tail. Find known and newly launched AI tools across the environment, not only a fixed set of integrated platforms.
2. Cover non-browser paths. Apply consistent policy across browser, desktop, CLI, IDEs, and non-browser agents, including AI agents running locally on employee devices.
3. Distinguish enterprise from personal tenants. Tell an approved enterprise account apart from a personal or free-tier one, so policy follows the data rather than the destination.
4. Decode modern protocols. Read prompts, responses, and tool calls natively across WebSockets, QUIC, Protobuf, JSON-RPC, APIs, and MCP, with conversation-level context carried across the exchange.
5. Enforce inline. Allow, coach, warn, block, and redact at the moment of use through Intentions, the application-specific modes that let policy distinguish acceptable AI use from risky behavior.

Aurascape does not reduce to shadow AI. It governs sanctioned and licensed tools through Intentions and entitlement-aware controls, applying policy by user role, account type, data sensitivity, and conversation context. In one Aurascape deployment, a global Fortune 200 healthcare technology enterprise drove unsanctioned, long-tail AI access and use outside licensed access to near zero across more than 60,000 users worldwide under one governance model (healthcare AI governance case study, Aurascape, 2026).

What Each Platform Inventories and Why the Gap Matters

Aurascape and Noma both discover AI well, but they inventory different objects. Noma builds an automatic inventory of the AI assets an enterprise operates: models, data pipelines, MCP servers, and homegrown agents. Aurascape’s patented discovery reaches the Commercial and Embedded AI the workforce adopts, plus the agents running locally on devices, across tens of thousands of applications.

New AI tools surface constantly, so catalog breadth and the speed of new coverage decide how much of the long tail a team sees. The size of the unseen estate is the argument for usage-side discovery: the Cloud Security Alliance found 82% of organizations have unknown AI agents operating in their environment, and only 21% maintain a real-time inventory of active agents (Cloud Security Alliance, 2026).

The two inventories answer different questions: the AI an organization built, and the AI its people picked up today. A team scaling agents without a usage-side inventory is the team CSA’s 21% figure describes from the wrong side.

Dual-Channel Signed Execution vs. Registry-Based Access Control

Model-only or tool-only governance leaves agents partly uncontrolled, because an agent runs on two legs: the intelligence channel to the model and the tool-execution channel to external systems. Aurascape governs both from one layer; Noma governs agent access and runtime behavior through identity and a registry. The distinction is breadth and mechanism, not detection versus enforcement.

Aurascape’s AI Proxy secures the intelligence channel between agent and model, and the Zero-Bypass MCP Gateway secures the tool-execution channel. In governed workflows, the gateway cryptographically signs approved tool calls and blocks unsigned ones, so a tool call that is not approved does not execute, with cross-call data lineage tracking data across chained actions (Aurascape, 2026). Treat MCP as one mechanism inside that tool-execution story, not the whole of agent security.

Noma governs agent access and runtime behavior across integrated environments: agent identity, a registry of MCP servers and tools, approve, review, or block access control, and runtime monitoring that can block. That is registry-based access control, strong where the agent estate is integrated and instrumented. Aurascape’s dual-channel signing reaches the broader execution path, including personal and enterprise context and local agents that never registered anywhere.

The exposure here is concrete. Censys observed more than 12,520 internet-accessible MCP services as of April 2026, and MCP does not require authentication by default, leaving most exposed services unauthenticated (Censys, 2026). In one Aurascape deployment, a Fortune 100 insurance and financial enterprise tripled its AI agent integrations with no unauthorized data access while protecting more than 20,000 users (insurance AI adoption case study, Aurascape, 2026).

Threat Coverage Spans the Intelligence and Tool-Execution Channels

Aurascape inspects both the agent-to-model channel and the agent-to-tool channel for prompt injection, jailbreaks, instruction override, and unsafe output, then enforces inline before data or an action reaches an external system. Noma inspects prompts, responses, tool calls, and agent behavior across the AI it is integrated with, and alerts, masks, or blocks on policy. Both apply runtime controls; the distinction is where they sit and how much of the AI surface they see.

These threats are reaching production now. OWASP ranks Prompt Injection as the top risk to LLM applications (LLM01), Sensitive Information Disclosure second (LLM02), and Excessive Agency sixth (LLM06), with indirect prompt injection the class most often cited in recent exploit disclosures (OWASP, 2025). EchoLeak, the zero-click indirect-injection flaw in Microsoft 365 Copilot tracked as CVE-2025-32711, showed injection reaching data exfiltration through a trusted, allowlisted channel before Microsoft patched it in 2025.

The deciding factor is not whether runtime controls exist. It is where they sit, how broadly they see AI use, and whether they govern the interaction and tool-execution path before sensitive data leaves or an unauthorized action runs. Aurascape inspects prompts and responses as observed traffic and enforces on them; it does not claim to read an agent’s hidden reasoning.

Which Platform Closes Your Highest-Priority AI Risk First

Choose by where your blind spots sit. Aurascape fits enterprises that need to govern employee AI use and AI development from one control plane; Noma fits engineering and security teams whose primary job is securing the models, pipelines, and agents they build and run. The deciding question is whether your gaps span the full workforce AI surface or sit mostly inside the AI your organization ships.

Aurascape deploys as an additive layer alongside existing Secure Service Edge (SSE), Secure Access Service Edge (SASE), Cloud Access Security Broker (CASB), and Data Loss Prevention (DLP) controls, with no rip-and-replace, which shortens the path to value when scope is wide. That additive model matters given how much of the risk is policy-level: Gartner attributes at least 80% of unauthorized AI transactions to internal policy violations rather than attacks (Gartner, 2025), exactly the behavior inline coaching and entitlement-aware policy are built to govern.

Proof in Production: Deployment Speed, Scale, and Regulatory Evidence

Aurascape’s usage-governance claims hold up in production deployments across regulated verticals. In one Aurascape deployment, a large transportation and logistics company went from proof of value to full deployment in about six weeks, starting with 400 users on day one and rolling out to 2,000, with sensitive-data interactions monitored across 100 percent of deployed users (transportation AI security case study, Aurascape, 2026).

For a regulated team that had considered blocking AI outright, one Aurascape banking deployment projected an 83 percent reduction in AI-based risk and a 27 percent productivity gain, with control mapping to GLBA, FFIEC, NCUA, and the NIST AI Risk Management Framework (Police Credit Union case study, Aurascape, 2026). Victor To, CISSP, Senior Security Architect, is a named reference on that engagement.

The recognition tracks the production results. Aurascape was a Top 10 Finalist in the 2025 RSAC Innovation Sandbox and has appeared on multiple CRN AI security and agentic AI startup lists across 2025 and 2026, including 10 Hot AI Security Startups to Know in 2025 and 12 Agentic AI Startups To Watch in 2026.

How Aurascape and Noma Compare for AI Governance Buyers

Security and compliance teams cluster around a small set of questions when they evaluate these platforms, and the answers fall along the build-side versus usage-side line. The table maps the capabilities the comparison hinges on: workforce coverage, tenant context, long-tail discovery, agent execution control, protocol decoding, and audit evidence.

Sources: Noma Security public materials, 2026; Aurascape, 2026.

Frequently Asked Questions

Why do Aurascape and Noma get evaluated against each other if they solve different problems?

Both are AI-native platforms that govern agents and MCP, so they show up on the same shortlists even though their centers of gravity differ. The evaluation usually resolves once a team decides whether its highest-priority blind spot is the AI it builds (Noma) or the AI its workforce and agents use (Aurascape).

How does AI Usage Control differ from AI Security Posture Management?

AI-SPM discovers and risk-scores the AI assets an organization builds and operates, such as models, pipelines, agents, and MCP servers, which is Noma’s center of gravity. AI Usage Control governs how people and agents use AI applications, copilots, and Embedded AI with context, intent, and entitlement, which is where Aurascape concentrates.

Does Noma govern employee use of consumer ChatGPT and Embedded SaaS AI?

Noma’s published coverage centers on integrated and instrumented AI: models, agents, MCP servers, and developer workflows. Governing the long tail of employee AI use, including consumer ChatGPT in a browser, Embedded AI inside SaaS, and personal accounts, is where Aurascape’s inline coverage across the endpoint, network, and API planes is the stronger fit.

Why does dual-channel agent control matter if a registry already governs access?

A registry governs which agents and tools are approved, but an agent still runs on two legs: the intelligence channel to the model and the tool-execution channel to external systems. Aurascape signs approved tool calls and blocks unsigned ones on the execution leg, which reaches local agents and personal context that never registered anywhere.

Which platform is the stronger fit for AI coding assistant governance?

Aurascape governs how developers use coding assistants across the IDE, CLI, desktop, and browser with policy by identity, intent, and entitlement plus signed approved tool calls. Noma is strong for red teaming and posture on coding agents an organization builds and runs, so the choice again follows the usage-side versus build-side line.

Does Aurascape replace a dedicated AI red-teaming tool?

No. Aurascape includes pre-deployment adversarial testing and runtime governance under See, Test, Protect, but a team whose primary requirement is continuous, dedicated automated red teaming should evaluate a purpose-built product such as Noma’s.

Does Aurascape require ripping out my SSE, SASE, CASB, or DLP?

No. Aurascape runs as an additive layer alongside existing controls and closes the AI visibility and governance gap at the interaction layer, inspecting modern protocols and agent tool calls that destination-based controls were not built to read.

How fast can a usage-governance deployment reach production?

Discovery typically surfaces shadow AI, embedded AI, and agents within days of deployment, before policy is fully tuned. In one Aurascape deployment, a transportation and logistics company went from proof of value to a 2,000-user rollout in about six weeks.

How Aurascape Governs Workforce and Agent AI Use From One Interaction Layer

The decision this comparison comes down to, securing the AI an organization builds versus governing how the whole workforce and its agents use AI, is the problem Aurascape was designed to close. The platform decodes prompts, responses, and tool calls at the conversation level across the endpoint, network, and API planes, then enforces inline before data leaves or an agent action runs. It governs sanctioned and unsanctioned use alike through Intentions and entitlement-aware policy, discovers shadow AI, personal accounts, endpoint-resident agents, and Embedded AI inside trusted SaaS, and runs as an additive layer alongside the existing stack.

Aurascape also covers the AI you build through See, Test, Protect, so a team that needs both the usage surface and the build side gets them from one platform. On the agent execution leg, the AI Proxy governs the intelligence channel and the Zero-Bypass MCP Gateway governs the tool-execution channel, signing approved tool calls and blocking unsigned ones with cross-call data lineage. That combination is why a Fortune 100 insurer tripled agent integrations with no unauthorized data access and a healthcare enterprise drove use outside licensed access to near zero across more than 60,000 users.

Aurascape is the AI Usage Control layer for the risk that sits on the usage side of the build-versus-use split this comparison drew. Every deployment runs through a tailored demo scoped to your AI security gaps.

See how Aurascape governs employee AI use, agent workflows, and approved tool execution from one interaction layer →