HEAD-TO-HEAD
Noma vs. Obsidian
Noma watches the agent. The breach plays out in Salesforce, Workday, and your third-party apps.
AI security findings can show that an AI system, model, or agent is risky. But the real business impact depends on what that system can reach: which SaaS apps, what permissions, which identities, and what sensitive data may be exposed.
AI inventories should do more than list models, agents, and apps. They should show which risks connect to sensitive SaaS data, privileged identities, service-account access, IdP gaps, and real activity. That way, teams know what to investigate, restrict, or remediate first.
Runtime and guardrail decisions shouldn’t rely only on AI-layer signals. They should also account for SaaS state, identity context, permissions, IdP federation, data sensitivity labels, and app activity. That way, teams can block or flag actions that create real business risk.
Noma is an AI security platform focused on helping teams discover, govern, and secure AI systems across the AI lifecycle, including models, agents, applications, and related AI risks.
Noma’s approach is built around the AI environment itself: what AI assets exist, how they are built, where risks appear, and how teams can govern AI development and usage. That gives security teams visibility into AI-layer risk and a way to apply controls across AI systems.
The gap is business context. AI systems don’t create risk in isolation. They connect to SaaS applications, identities, service accounts, OAuth grants, permissions, and sensitive data. AI-layer visibility can show that an AI asset or agent is risky, but it may not show what the receiving app allowed, what identity was used, what permissions were consumed, or what downstream activity occurred.
Both companies are in AI security. The difference is the data each platform reads. Noma focuses on the AI layer. Obsidian connects AI activity to the SaaS and identity context that determines whether the activity is actually risky.
Obsidian's Knowledge Graph ties identity, permissions, token grants, integrations, and activity together across every connected application. When a third-party vendor is compromised, Obsidian doesn't wait for the disclosure. Network effects mean that signal is already flowing across every environment we protect.
The result is faster investigations, cleaner blast radius attribution, and remediation decisions backed by what actually happened, not what could have.
Both platforms help teams manage AI risk. The difference is the layer each platform prioritizes and the data each one reads.
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Both enforce at runtime. The difference is what each policy reads when it fires. Agent-side runtime can control what an agent does at the tool-call level. Obsidian extends runtime decisioning with SaaS state, identity, and permissions, so policies can be more specific over time: for example, don't touch a file in OneDrive labeled sensitive, when the agent was built by a specific user, calling on behalf of a specific identity. That granularity comes from reading the receiving app's state and identity context, not just the agent's tool calls.
Inventory is only the starting point. Obsidian shows who built the agent, who ran it, and what the service account inside the SaaS app is permissioned to do. That gives defenders the context to understand which agents create real business risk and align configuration to that risk.
Noma ships strong out-of-the-box framework mappings for NIST AI RMF, ISO 42001, EU AI Act, and OWASP. The harder question is whether a checklist-passing AI inventory catches the breach. An agent can clear every governance control on paper and still hold a toxic combination of entitlements only visible from inside the connected SaaS apps. Obsidian builds the evidence layer the governance reporting actually needs.
You'd be paying twice for the AI-layer data. Both platforms connect to the same AI platforms with effectively the same connectors and ingest the same underlying telemetry. The cleaner answer is to pick the platform that reads the SaaS and identity context the other can't.
Posture detection tells you an agent looks risky. By the time a posture finding lands, the agent has often already executed. Runtime enforcement fires at the tool call, before the action completes. Both platforms have moved to runtime for this reason. The remaining question is what each runtime can read: agent inputs and outputs alone, or also the SaaS state and identity context that decide whether the action is actually risky.
99.99% uptime over the last 12 months. Regional hosting across the US, Europe, Saudi Arabia, and Australia. Granular RBAC scoped per app. Production-safe connectors with bulk-API support. Obsidian connects to your most critical SaaS apps and collects activity data without disrupting them. Learn more about our certifications and attestations.
These come from real customer environments, including customers who've evaluated Noma and Obsidian.
See what gives Obsidian the edge over others
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