How is DeepInspect different from Obsidian Security?

Obsidian Security monitors the SaaS and identity layer: application configurations, OAuth grants, login behavior, and which AI agents exist across connected apps. It detects anomalies and misconfigurations on a continuous or near-real-time scanning cadence. DeepInspect operates one layer down, on the actual HTTP request between an authenticated caller and an LLM. It evaluates identity and policy on every individual call and makes a pass, redact, or block decision before the model sees the request, then writes a signed audit record of that decision. Obsidian tells you what exists and who has access to it. DeepInspect tells you what happened on a specific request and proves it.

Can Obsidian Security replace DeepInspect?

Obsidian Security does not sit inline on LLM API traffic and has no mechanism to evaluate or block the content of a specific prompt before it reaches a model. Its detections come from SaaS configuration APIs and identity provider logs, plus OAuth grant metadata, none of which carry the actual request payload sent to an LLM. A team relying on Obsidian alone would have strong visibility into SaaS and identity risk but no way to answer which specific AI request carried restricted data or whether that request was authorized under policy.

Can DeepInspect replace Obsidian Security?

DeepInspect does not monitor SaaS configurations or audit OAuth grants, and it does not detect account takeover across an identity provider or connected SaaS applications. Those are outside its enforcement boundary, which is limited to HTTP AI traffic between authenticated users or agents and LLMs. A team relying on DeepInspect alone would have strong request-level AI enforcement but no visibility into whether a compromised account or an overprivileged SaaS integration exists elsewhere in the environment.

Most regulated teams end up running both because they answer different audit questions. Obsidian covers "is our SaaS and identity estate configured safely, and has any account been compromised." DeepInspect covers "was this specific AI request authorized, and can we prove it." A security review or a regulator can ask either question independently, and neither product can answer the other's question from its own data.

Does Obsidian Security detect prompt injection or data exfiltration inside an LLM conversation?

Obsidian's visibility comes from SaaS and identity APIs, not from the request stream between a caller and a model. It can flag that an AI agent's OAuth grant looks unusual or that a service account is pulling data at an abnormal rate, which are useful adjacent signals. It has no mechanism to inspect a specific prompt for injected instructions or a specific response for exfiltrated data, because that content does not pass through a SaaS configuration endpoint.

Does DeepInspect discover shadow AI agents across our SaaS estate?

DeepInspect's visibility is scoped to HTTP traffic that routes through the proxy on its way to an LLM. It does not scan SaaS tenants or identity providers to inventory which AI agents or integrations exist, which is the discovery problem shadow AI monitoring at the SaaS layer is built to solve. If a shadow agent's traffic never routes through DeepInspect, DeepInspect has no record of it, which is a reasonable argument for pairing the two.

What happens when an OAuth grant Obsidian flags is also making LLM calls?

Obsidian's alert tells a team an agent or integration holds a grant worth reviewing, based on scope or behavior. That alert does not, by itself, stop the next request that agent sends to a model. If the agent's traffic also routes through DeepInspect, the flagged identity's requests can be evaluated and blocked or redacted at the policy layer immediately, independent of how long the SaaS-side review takes to resolve.

Is DeepInspect a Obsidian Security competitor or a complementary layer?

The two are complementary layers rather than direct substitutes, similar to how a building's perimeter alarm and its door locks solve related but distinct problems. Obsidian secures the SaaS and identity perimeter around AI usage. DeepInspect enforces policy on the individual AI requests that cross that perimeter. Procurement conversations that treat them as a single line-item decision usually end up buying both once the audit question narrows to a specific request instead of a general posture score.

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August 23, 2026

DeepInspect vs Obsidian Security: Inline AI Enforcement vs SaaS Identity Threat Detection

Obsidian Security detects account takeover, risky OAuth grants, and shadow AI usage across the SaaS estate, scored on a monitoring cadence. DeepInspect enforces identity-bound policy on the live request before it reaches the model and writes a signed audit record. Teams evaluating an Obsidian Security alternative for AI request control need to know these sit at different layers.

ByParminder Singh· Founder & CEO, DeepInspect Inc.

Comparisons & Alternativesllm-securityai-securityzero-trustpolicy-enforcementforensic-auditinline-enforcementshadow-ai

DeepInspect vs Obsidian Security: Inline AI Enforcement vs SaaS Identity Threat Detection

A security architect I spoke with in June had Obsidian Security wired into her Okta tenant and forty-one connected SaaS applications. It flagged an OAuth grant to an unsanctioned AI note-taker three days after a sales rep approved it, and it caught a service account pulling an unusual volume of Salesforce records overnight. Both were real findings. Neither one told her whether the LLM call her underwriting team made an hour earlier, carrying a customer's SSN, was something that specific analyst was authorized to send.

That's the gap this piece is about. Obsidian Security and DeepInspect get compared because both sit in AI security conversations and both touch identity. The overlap stops there. Obsidian watches the SaaS and identity estate for signs something already went wrong, or is configured to go wrong. DeepInspect decides, on every single AI request, whether that request is allowed, before the model ever sees it.

TL;DR

Obsidian Security is an SSPM and Identity Threat Detection and Response platform: it scans SaaS configurations and OAuth grants, and now maps shadow AI apps and agents.
DeepInspect is a stateless inline proxy that enforces identity-bound policy on every LLM API call, in real time, before the request reaches the model.
Obsidian answers "was an account compromised, or is a risky app connected" on a scan cadence. DeepInspect answers "should this specific request happen right now," on every request.
Obsidian's detections happen after a grant exists or a login occurs. DeepInspect's decisions happen before a prompt reaches the model, with a signed record of the outcome.

Obsidian Security

Obsidian Security, founded in 2017 by Glenn Chisholm, Ben Johnson, and Matt Wolff, builds a platform that protects the applications, identities, integrations, and now AI agents that sit inside an organization's SaaS estate. It connects to the SaaS applications a company already runs, Salesforce, Microsoft 365, Workday, Okta, and reads configuration state, identity activity, and integration grants through those APIs.

Two capabilities anchor the platform. SaaS Security Posture Management continuously scans SaaS configurations and flags overprivileged accounts, then runs compliance checks against frameworks like SOC 2 or ISO 27001. Identity Threat Detection and Response watches human and non-human identities for account takeover attempts, MFA bypass and session hijacking, plus OAuth token abuse, tying together login anomalies and API behavior to catch a compromised account or a risky third-party grant.

Obsidian extended into AI security by mapping AI agents and integrations across the connected SaaS estate. According to Obsidian's own platform overview, it correlates each AI agent, whether built on Copilot Studio, Salesforce Agentforce, Amazon Bedrock, Google Vertex AI, or Azure AI Foundry, with its creator, its OAuth grants, and the live entitlements those credentials carry. That is a genuinely useful inventory question: which agents exist, who made them, and what can they touch.

The operating cadence is detection and response against the SaaS and identity layer. Configuration scans, OAuth grant correlation, and behavioral baselining run continuously or in near-real-time, and an alert fires when something drifts from the baseline or matches a known abuse pattern. That is a different question from whether one specific HTTP request, from one specific authenticated caller, carrying one specific payload, should be allowed to reach an LLM right now.

The inline enforcement proxy

DeepInspect is a stateless proxy that sits inline 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 or monitoring loop. DeepInspect reads the identity context the calling application supplies, checks the role or authorization tied to that identity, and applies the policy decision point governing that combination of identity, data classification, and destination model, then passes, redacts, or blocks the call before it reaches the model. The proxy works the same way in front of OpenAI, Anthropic, Bedrock, Azure OpenAI, or a self-hosted endpoint, because it operates on the HTTP layer rather than inside a SaaS connector or an agent-builder's own logging. 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 collapsed to 22 seconds. A detection that surfaces on a monitoring cycle, however tight, is still a decision made after the window closed.

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, which keeps the evidence outside the calling application's own custody. This matters for the same reason Obsidian's shadow AI detection matters at the SaaS layer: an agent built with a valid OAuth grant can still send a prohibited prompt, and posture monitoring at the app-integration level has no visibility into what that agent actually asked the model.

Feature comparison

The two products rarely land on the same line item in a procurement review, because they instrument different layers of the same AI deployment.

What it monitors: Obsidian monitors SaaS configurations and OAuth grants, plus identity behavior across connected applications and agents. DeepInspect monitors and gates the actual HTTP request content flowing between a caller and an LLM.
When it acts: Obsidian scans continuously or near-real-time and cross-references what it finds, surfacing an alert once a pattern matches. DeepInspect evaluates before the request reaches the model, on every call.
Enforcement capability: Obsidian surfaces alerts and can revoke a flagged OAuth grant or suspend a compromised account. DeepInspect makes a pass, redact, or block decision on the request itself, inline, before the model processes it.
Identity awareness: Obsidian ties findings to the SaaS identity and its connected app grants. DeepInspect binds every decision to the authenticated caller's identity and role at the moment of the request.
Audit output: Obsidian produces posture reports and compliance mappings, plus threat alerts. DeepInspect produces a signed, per-decision audit record built for regulatory disclosure.
Coverage boundary: Obsidian's visibility depends on a SaaS or identity-provider connector existing for the application or agent platform in question. DeepInspect's visibility depends on the HTTP traffic routing through the proxy, independent of which SaaS platform originated the call.
Primary buyer: Obsidian is typically bought by SaaS security or identity teams managing account risk and app sprawl. DeepInspect is typically bought by security, compliance, or platform teams that own AI access control and audit obligations.
Failure mode: Obsidian's detections degrade gracefully; a missed connector means a blind spot, not an outage. DeepInspect can fail closed, denying a request by default when policy evaluation is ambiguous or unavailable.

Pick Obsidian Security if...

Obsidian fits teams whose open question is still about the SaaS and identity estate, not about individual AI request content.

Your priority is discovering which SaaS applications, integrations, and AI agents exist across the organization, who created them, and what OAuth scopes they hold.
You need account takeover and MFA bypass detection, plus session hijacking coverage, across your identity provider and connected SaaS tenants.
You want continuous configuration hardening and compliance mapping (SOC 2, ISO 27001) for the SaaS applications your teams already run.
Your open question is "what is connected and who has access," not "should this specific prompt be sent right now."

Pick DeepInspect if...

DeepInspect fits teams whose question has moved from SaaS posture to the content and legitimacy of individual AI requests.

You need per-request, identity-aware enforcement, blocking, or redaction on live AI traffic as it happens, not a posture score updated on a monitoring cycle.
A regulator or auditor can ask "who sent this data to a model, under what policy, and can you prove it" and you need a direct answer, not a reconstructed timeline from SaaS logs.
You are running toward the EU AI Act's Article 12 logging deadline, which takes effect August 2, 2026, and your evidence needs to come from a system the calling application cannot alter after the fact.
You need enforcement that holds in front of any LLM endpoint regardless of which team stood up which agent platform this quarter, something shadow AI makes harder to track by SaaS inventory alone.

Teams that end up running both rarely planned it that way upfront. Obsidian typically shows up first, bought by an identity or SaaS security team trying to get a handle on app sprawl and OAuth risk. DeepInspect tends to arrive once someone in compliance or legal asks a narrower question that posture monitoring was never built to answer: not which agents exist, but what a specific one said to a model last Tuesday. Watching a mature Obsidian rollout stall in an audit over that exact gap is avoidable, and a little embarrassing given how much visibility the team already had.

DeepInspect

This is the request-level gap Obsidian's SaaS and identity monitoring was never built to close. Obsidian can tell a security team that an AI agent holds a broad OAuth grant into Salesforce. It cannot tell them what that agent's last hundred prompts to the underlying LLM actually contained, because that content never touches a SaaS configuration API or an identity provider's login log.

DeepInspect sits at the AI request boundary as a model-agnostic proxy, evaluating identity, role, and data classification on every call and producing a signed audit record for every decision. It runs alongside SaaS posture and identity monitoring rather than replacing it: Obsidian answers whether the accounts and integrations around your AI estate are configured safely, DeepInspect answers whether a specific request should have happened and proves the answer months later. The same enforcement-versus-detection split shows up in The Post-Authentication Gap in AI Agent Security: a valid login or a valid OAuth grant is not the same thing as a valid individual action.

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

Frequently asked questions

How is DeepInspect different from Obsidian Security?: Obsidian Security monitors the SaaS and identity layer: application configurations, OAuth grants, login behavior, and which AI agents exist across connected apps. It detects anomalies and misconfigurations on a continuous or near-real-time scanning cadence. DeepInspect operates one layer down, on the actual HTTP request between an authenticated caller and an LLM. It evaluates identity and policy on every individual call and makes a pass, redact, or block decision before the model sees the request, then writes a signed audit record of that decision. Obsidian tells you what exists and who has access to it. DeepInspect tells you what happened on a specific request and proves it.
Can Obsidian Security replace DeepInspect?: Obsidian Security does not sit inline on LLM API traffic and has no mechanism to evaluate or block the content of a specific prompt before it reaches a model. Its detections come from SaaS configuration APIs and identity provider logs, plus OAuth grant metadata, none of which carry the actual request payload sent to an LLM. A team relying on Obsidian alone would have strong visibility into SaaS and identity risk but no way to answer which specific AI request carried restricted data or whether that request was authorized under policy.
Can DeepInspect replace Obsidian Security?: DeepInspect does not monitor SaaS configurations or audit OAuth grants, and it does not detect account takeover across an identity provider or connected SaaS applications. Those are outside its enforcement boundary, which is limited to HTTP AI traffic between authenticated users or agents and LLMs. A team relying on DeepInspect alone would have strong request-level AI enforcement but no visibility into whether a compromised account or an overprivileged SaaS integration exists elsewhere in the environment.
Do we need both?: Most regulated teams end up running both because they answer different audit questions. Obsidian covers "is our SaaS and identity estate configured safely, and has any account been compromised." DeepInspect covers "was this specific AI request authorized, and can we prove it." A security review or a regulator can ask either question independently, and neither product can answer the other's question from its own data.
Does Obsidian Security detect prompt injection or data exfiltration inside an LLM conversation?: Obsidian's visibility comes from SaaS and identity APIs, not from the request stream between a caller and a model. It can flag that an AI agent's OAuth grant looks unusual or that a service account is pulling data at an abnormal rate, which are useful adjacent signals. It has no mechanism to inspect a specific prompt for injected instructions or a specific response for exfiltrated data, because that content does not pass through a SaaS configuration endpoint.
Does DeepInspect discover shadow AI agents across our SaaS estate?: DeepInspect's visibility is scoped to HTTP traffic that routes through the proxy on its way to an LLM. It does not scan SaaS tenants or identity providers to inventory which AI agents or integrations exist, which is the discovery problem shadow AI monitoring at the SaaS layer is built to solve. If a shadow agent's traffic never routes through DeepInspect, DeepInspect has no record of it, which is a reasonable argument for pairing the two.
What happens when an OAuth grant Obsidian flags is also making LLM calls?: Obsidian's alert tells a team an agent or integration holds a grant worth reviewing, based on scope or behavior. That alert does not, by itself, stop the next request that agent sends to a model. If the agent's traffic also routes through DeepInspect, the flagged identity's requests can be evaluated and blocked or redacted at the policy layer immediately, independent of how long the SaaS-side review takes to resolve.
Is DeepInspect a Obsidian Security competitor or a complementary layer?: The two are complementary layers rather than direct substitutes, similar to how a building's perimeter alarm and its door locks solve related but distinct problems. Obsidian secures the SaaS and identity perimeter around AI usage. DeepInspect enforces policy on the individual AI requests that cross that perimeter. Procurement conversations that treat them as a single line-item decision usually end up buying both once the audit question narrows to a specific request instead of a general posture score.

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