Token Observe vs WitnessAI
WitnessAI stands in front of the interaction and classifies the intent. Token Observe stands in front of the API call and decides the action, its cost and its evidence.
2 September 2026
- WitnessAI — home
- WitnessAI — platform
- WitnessAI — Observe
- WitnessAI — Control
- WitnessAI — Protect
- WitnessAI — for developers
- WitnessAI — for applications
- WitnessAI blog — how to enforce AI policies with runtime controls
- WitnessAI blog — introducing Agentic Control
- WitnessAI blog — SOC 2 Type II attestation
- WitnessAI blog — FinOps for AI
- WitnessAI blog — cost per AI query
- WitnessAI whitepaper — Secure AI Enablement Platform
- WitnessAI solution brief — Witness/Anywhere
- WitnessAI on AWS Marketplace — vendor listing and pricing dimensions
Their claims, not our testing. Verify anything that decides it for you.
On this page
On coverage of the estate and on detection quality, WitnessAI is the better purchase for most readers, and Token Observe’s own roadmap names it as a source to consume from rather than a rival
Start with the part Token Observe cannot answer. WitnessAI’s product page claims you see AI activity across the entire network without relying on browser extensions or endpoint clients, and names coverage of native applications such as Windows 11 Copilot and Office 365 that a browser-proxied tool would miss; their Observe page claims detection of more than four thousand AI applications and a scan of the whole network for third-party AI applications and agents; their developer page names GitHub Copilot and Cursor and says the platform operates at the network level with no new SDKs or additional clients; and the Witness/Anywhere solution brief extends that to employees working outside the corporate network, supporting more than 2,700 AI application URLs and deployable through CrowdStrike EDR, Jamf, Kandji or Fleet MDM, group policy or Microsoft Intune, enterprise browsers or DHCP. Their whitepaper describes integrating with the proxy, firewall and secure service edge you already run. Token Observe governs what presents a credential it issued, and nothing else. Its own compliance mapping lists anything about agents that never route through the gateway under what the product does not evidence, its support boundary says chasing those agents down inside your organisation is your work, and the shadow-AI radar only reconciles the bills, egress logs, service-account key audits and IDE telemetry you actually feed it. If shadow AI across a workforce is the problem, that gap decides the purchase on its own.
Detection is their product and it is not Token Observe’s. WitnessAI publishes seven integrated guardrails — Data Protection, Risk Activity, Model Identity Protection, Model Protection, Behavioral Activity, Organizational Behavior and Harmful Response Prevention — built on intent classification that their whitepaper illustrates with the difference between summarising legal documents and sharing privileged information, and with intention-based jailbreak attempts phrased as a question about how a hacker would bypass something. Their Control page describes going beyond regex-based rules; their Protect page describes bidirectional inspection that secures prompts before models and agents process them and filters outputs before users see them, with tokenisation of personally identifiable information, credentials and secrets; their home page announces NER-D as a model for context-aware sensitive-data detection. Token Observe ships nine weighted injection heuristics scored 1.25 times higher on tool results, and eleven sensitive-data classes of which exactly three are checksum-validated, with free-text personal data not detected at all. Its own documentation calls that a compensating control and not the customer’s only data-loss-prevention layer, and puts the residual risk on a register requiring a named acceptor. Against a maintained detection product, a nine-pattern heuristic should lose, and the honest thing is to say so before a buyer discovers it.
Then there is procurement, which decides more deals than either of the above. WitnessAI publishes a completed SOC 2 Type II attestation covering security, availability, confidentiality, processing integrity and privacy, says each control was tested for design and for consistent execution over time, offers the full report to customers and partners under non-disclosure, and states an intention to recertify annually. Their AWS Marketplace listing publishes prices, contract terms and support tiers, and describes single-tenant isolation with multi-region deployment for data residency and sovereignty. Token Observe holds none of that: no SOC 2, no ISO 27001, no ISO 42001, no independent penetration-test result, no availability commitment, no published price list, and a licence that is a template pending review by counsel rather than an executed grant. If your gate is an attestation or a signed uptime number, the comparison ends here in WitnessAI’s favour. Their post says the review covered the full scope of their security and governance practices and that each control was tested over the audit period, without naming that period, so ask them for the report itself — that is the only version of the answer worth having, and this page does not hold it for them.
Token Observe and WitnessAI, capability by capability
The WitnessAI column paraphrases WitnessAI’s own published material as it stood on 2 September 2026. None of it has been independently tested here, products in this category ship quickly, and a capability that is absent from a vendor’s documentation is not the same thing as a capability the product lacks. Check anything that decides it for you against their own current documentation.
Where it sits
The network level. Their product page describes seeing AI activity across the entire network without browser extensions or endpoint clients, and their whitepaper diagrams a proxy service between employees and both public AI models and an internal engineering model, integrating with the proxy, firewall and secure service edge you already run.
A gateway the agent’s own traffic passes through. Eleven ordered steps in one process — authenticate, resolve agent, open trace, sanitise, scan, govern, enact, route, call upstream, govern the response, meter and record.
Both are inline. They are inline at different points, and an estate can hold both without either becoming redundant.
No client-side change is required in the supported model: their developer page says the platform operates at the network level with no new SDKs or additional clients, and their applications page says no SDKs and no invasive instrumentation. A team that has built its own agent is described in their Agentic Control post as attaching the same runtime protection through two REST API endpoints on its orchestration layer. For users outside the network, Witness/Anywhere is described as agentless, plug-in free endpoint integration deployed through CrowdStrike, Jamf, Kandji, Fleet, group policy, Intune, enterprise browsers or DHCP.
One environment variable. For supported OpenAI-compatible, Anthropic and Gemini ingress a base-URL change is normally the whole integration, plus one Streamable HTTP endpoint for MCP.
Their Observe page claims detection of more than four thousand AI applications and a network-wide scan for third-party AI applications and agents; Witness/Anywhere is described as supporting more than 2,700 AI application URLs across laptops, desktops and mobile devices.
Only what presents a credential Token Observe issued. Everything else is a shadow-AI radar finding assembled from five evidence sources you feed it, and a source may only clear a finding when its run actually completed.
This row goes their way, and it goes their way by a distance. The two products are not sized against the same estate.
Governed across providers rather than by provider dialect. Their Agentic Control post says an approved-tool policy is enforced organisation-wide across every application and model provider and that the same call routed through a different provider only meets the same rule; their policy-enforcement post describes per-model controls because providers differ in data handling and risk characteristics; their Control page describes routing prompts to the right models; and their whitepaper describes in-line prompt redirection sending prompts containing sensitive code from GitHub Copilot to an internal model. A published list of named provider API dialects accepted and governed as first-class upstreams is not described in their published material as of 2 September 2026.
First-class: OpenAI, Anthropic, Gemini, OpenRouter, Amazon Bedrock and Azure OpenAI, with identical policy, redaction, budgets and tracing enforced by a table-driven test over every provider kind — because a policy that fires on OpenAI but not on Gemini is worse than no policy.
Their Agentic Control post describes an MCP Catalog scoring each server against OWASP and CVE risk classes, a security team approving which MCP servers and tools agents may use, that policy enforced organisation-wide, anything off the list denied before the tool executes, and a ban set organisation-wide that cannot be quietly re-enabled or routed around by switching providers. Their applications page states the same thing as enforcing an approved list of MCP servers and tools at the tool-call level for every agent.
One endpoint in front of every registered upstream server. Tools reach an agent namespaced and filtered to its grants, every call is authorised again at execution because filtering a list is a usability feature rather than access control, and each descriptor is hashed at approval so an upstream rewrite quarantines the tool until a human approves it again.
The closest row on the page. Both refuse a tool call before it executes; they differ on what the refusal is keyed to — an organisation-wide catalogue entry, or this agent’s grants plus the exact descriptor hash.
Their Agentic Control post says attribution ties each invocation back to the human who triggered it, agent-to-agent calls included, and their home page describes tracing every agent action to a human identity for audit.
The named human’s directory groups intersect the agent’s authority after the verdict and before the approval branch, so the mask can only ever narrow what the agent was already allowed. It is off by default, and two of its three settings refuse nothing.
What it enforces
A four-action model on their policy-enforcement post: allow, which lets compliant interactions proceed while keeping a full audit trail; warn, which surfaces a policy alert to the user at the moment of risk without blocking; block, described as pre-execution protection before the interaction reaches the model; and route, which redirects sensitive queries instead of blocking them and can send a query to an approved internal model or apply real-time data tokenisation.
Allow, block, redact or require approval, resolved at one decision point rather than by a set of middlewares that can each decide something different, plus a kill switch checked first in the pipeline and scoped to one agent, one team or the whole estate.
Intent-based. Their Control page describes going beyond regex-based rules to policies that adapt to what users are actually trying to do; their home page describes policies by department, role, intent or workforce type; their policy-enforcement post describes organisation-wide baselines, team-level policies, individual exceptions and per-model controls.
A trigger, an action and a scope. Seven trigger kinds — tool and argument values, model and estimated size, spend, rate, detected data classes, injection score and source, and hour of day — resolved to a single verdict.
Different shapes for different jobs. Intent classification answers what is this person trying to do; the trigger model answers what about this payload changes the answer.
Detection is the product. Seven integrated guardrails in their whitepaper, ten modelled dimensions of prompt risk under Risk Activity, adversarial attack and prompt-injection defence under Model Protection, and NER-D announced on their home page as a model for context-aware sensitive-data detection.
Detection is one policy input among seven trigger kinds, published with its confidence scores so a policy can set its own threshold: nine weighted injection patterns scored 1.25× higher on tool results, and eleven data classes of which three are checksum-validated. Free-text personal data is not detected at all.
This row goes their way too, and Token Observe’s own documentation concedes it: heuristic detection has false negatives, and the product is described as a compensating control rather than the customer’s only data-loss-prevention layer.
Their whitepaper says Data Protection can operate seamlessly, can train users through the use of warnings, or can block sensitive data transmission, and the warn action on their policy-enforcement post surfaces an alert without blocking. A monitor-only mode that records what a rule would have done without affecting the user is not described in their published material as of 2 September 2026.
Shadow mode on every rule, recording what it would have done while changing nothing about the response. Where the deployment turns the gate on, no rule may begin enforcing until a backtest of that exact rule against recorded traffic is acknowledged by a named person.
Approval in their published material is a security team approving which MCP servers and tools agents may use, enforced organisation-wide. A per-request approval gate that parks one pending action on a named person is not described in their published material as of 2 September 2026.
A policy action that refuses with 403, mints an approval bound to the SHA-256 of the canonicalised action plus its execution context, and holds the trace open. Single-use through a compare-and-set, expiring at 60 minutes by default and one minute to seven days by policy. Change one argument and the retry is refused as a mismatch rather than allowed as near enough.
Approving pushes nothing to the agent. Token Observe has no way to call an agent back; the agent redeems the approval by repeating the identical request with its id.
Cost appears as a routing input and an attribution output rather than as a ceiling. Their product page describes routing requests based on risk, cost and purpose; their FinOps post describes network-level visibility making cost attribution and governance measurable instead of reactive, and names poorly governed inference and unthrottled endpoints generating large bills within hours as part of the problem it addresses; their cost-per-query post describes routing that keeps premium-model spend where it earns its price. A hard per-user or per-agent spend cap that refuses a request once a threshold is crossed is not described in their published material as of 2 September 2026 — on the two cost posts read for exactly that question.
Hard USD ceilings per request, per rolling hour, per UTC day and per UTC month, alongside requests, tool calls and tokens per minute, reserved in one per-agent database transaction before egress. A budgeted agent whose resolved route has an unpriced reachable target is refused before egress rather than priced at zero, because an empty price table is exactly how the ceiling was once silently disarmed.
What it records
Their Agentic Control post says every blocked call writes an audit record naming the user, the agent, the tool, and the rule that denied it.
A trace id is minted before the verdict, so a blocked request is recorded rather than absent. The caller receives a typed ACP_POLICY_BLOCKED error and the trace id in a response header, and the trace closes as blocked with the policy that fired.
Their policy-enforcement post describes interaction-level logs including both prompts and AI-generated responses, and bidirectional defence logging of both; the Witness/Anywhere brief says conversational detail is captured to enable analysis while respecting privacy and compliance requirements.
Governed requests, tool calls, results, usage and policy decisions, with redaction applied before storage and secrets never tokenised reversibly — in a timeline written for a compliance officer rather than for the engineer who wrote the agent.
Their AWS Marketplace listing says immutable audit trails support compliance reporting for frameworks including the EU AI Act, ISO/IEC 42001, the NIST AI Risk Management Framework and PCI DSS 4.0.1, and their Observe page separately describes single-tenant isolation with customer-controlled encryption. The construction behind the immutability claim is not described in their published material as of 2 September 2026.
Stated in three layers with the weakest named first: unkeyed SHA-256 by default, which an operator with write access can rewrite and recompute and which the repository ships a forgery test to prove; HMAC-SHA256 once an off-box MAC key is configured; Ed25519 anchoring of the chain head to an off-box sink once a signing key is set. Every verification result reports which of the three you are holding.
Tamper-evident, not tamper-proof. What an anchor buys is exactly one thing: any copy you kept off-box beats any rewrite made after you took it.
Their Observe page describes automatically cataloguing AI apps and agent activity for efficient compliance and reporting, and identifying every AI tool, agent and conversation so a team can see who is using what and which agents are running. A query language, an API contract or an export format for that record is not described in their published material as of 2 September 2026.
A question in English translated into a validated filter object over fourteen allow-listed fields — never into SQL, because trace content is attacker-influenced by construction — and shown back as editable chips. It cannot group, count or correlate across traces, and it degrades to a deterministic keyword parser when no model is configured.
Retention periods are not stated in their published material as of 2 September 2026. Their AWS Marketplace listing describes multi-region deployment supporting data residency and sovereignty requirements.
Default trace retention is keep-forever, which is a storage-growth decision you make deliberately rather than a tier you buy. The capacity documentation publishes the growth curve and its methodology instead of a retention default.
Granular audit trails for compliance and reporting, mapped on their Marketplace listing to the EU AI Act, ISO/IEC 42001, NIST AI RMF and PCI DSS 4.0.1, with their SOC 2 Type II report available to customers and partners under non-disclosure.
A compliance export bundling the traces and events for the period, the approvals with approver identity and rationale, the audit entries covering every governance-plane change, and a chain verification result naming the sequence number of any break — sealed with a SHA-256 digest at a recorded time. The bundle is not itself signed.
How it deploys and what it costs
Software as a service, listed as deployed on AWS on their Marketplace listing, with single-tenant isolation described as keeping data under customer control, customer-controlled encryption, and multi-region deployment supporting data residency and sovereignty — claims their Observe page carries too, under Enterprise Architecture.
Self-hosted only, at every tier. One Node process and one SQLite file in write-ahead-log mode, with PostgreSQL behind the store ports as an evaluation alternative rather than a supported high-availability topology.
A hosted single-tenant instance processes the interactions it governs, and their material describes capturing prompts and responses. What else is processed or retained, and for how long, is a question for their data-processing terms rather than for this page.
Nothing. No product telemetry, no phone-home, no prompts, no keys, no trace database. Governed payloads leave your network only for the model and tool providers you configure, after policy and redaction, and the runtime data flow is documented so it can be verified rather than trusted.
Witness/Anywhere, described as bridging the gap for employees who bypass enterprise proxy or secure service edge infrastructure, deployed through CrowdStrike EDR, Jamf, Kandji or Fleet MDM, group policy or Intune, enterprise browsers or DHCP.
Out of scope by design. Token Observe governs agents holding a credential it issued; its endpoint hook for managed developer subscriptions reports itself as preview and not production-eligible, and should not be bought as an equivalent to an inline network control on an unmanaged device.
Their whitepaper lists integration with existing security tools and identity provider among its implementation claims, their Marketplace listing says optional integrations with identity and endpoint systems are available to extend coverage, and their Control page describes customising controls for specific departments, roles or use cases. Named federation protocols and provisioning standards are not described in their published material as of 2 September 2026.
OIDC single sign-on with bounded SCIM Users provisioning for viewer accounts, which permits disable but not rename or reactivation once an account holds more authority, and revokes sessions on disable. SAML, SCIM Groups and live-directory reads are deliberately not built, and the architecture document names them.
Published on their AWS Marketplace listing as twelve-month contracts across three dimensions: $180 per user with a 1,000-user minimum, $24 per agent for Agentic Visibility with a 2,500-agent minimum, and a $300,000 annual fee for the WitnessProtect model-protection guardrail, with consumption beyond commitment metered at $0.01 a unit for true-ups. Those are the listed prices on that channel; the same listing invites a private offer for custom terms or enterprise pricing, so a negotiated figure may differ.
No published price list. The licence is commercial source-available — use, modify and self-host, with redistribution and offering it as a competing hosted service excluded — and it is a template pending review by counsel rather than an executed grant.
A completed SOC 2 Type II attestation covering security, availability, confidentiality, processing integrity and privacy, with each control tested for design and for consistent execution over time, the full report available to customers and partners on request under non-disclosure, and a stated intention to continue certifying annually. Their post describes the review as covering the full scope of their security and governance practices; it does not name the audit period, so ask them for the report itself.
None held: no SOC 2, no ISO 27001, no ISO 42001, no independent penetration test. What exists instead is a published residual-risk register, a published defect list naming the attacks that still work, and a licence drafted to permit a pre-purchase test with no gag clause and no pre-approval of results.
Their Marketplace listing describes standard support included with every subscription, with email-based technical support and a next-business-day response; and premium support as an add-on, with a named technical account manager, a four-hour response SLA for critical issues and a direct Slack channel to their engineering team.
No availability SLA, and the reason is stated rather than negotiated: the vendor does not operate your deployment and holds no telemetry from it, so an uptime number from that party would be unmeasurable by either side.
Two different requests, and what each product is holding when it decides
WitnessAI is standing in front of an interaction. On their published flow the traffic reaches a proxy service — theirs, or the proxy, firewall or secure service edge you already run, which their whitepaper describes integrating with — and what arrives is a prompt from a person or an agent heading for a public model, an internal model, or a tool. The decision is made on what that prompt appears to be for. Their whitepaper’s own examples are the useful ones: distinguishing an employee in marketing summarising public records from one scraping sensitive user data, distinguishing summarising legal documents from sharing privileged information, and catching an intention-based jailbreak phrased as a question about how a hacker would bypass something. Four actions follow — allow, warn, block, route — and the route action is the interesting one, because their whitepaper describes redirecting a prompt containing sensitive code away from GitHub Copilot and into an internal engineering model rather than refusing the person outright.
Token Observe is standing in front of an API call from an agent whose identity it issued. That is a much narrower position and it buys a different class of rule, because the identity on the request is not merely observed — it carries a permission set, a budget, a lifecycle status, a risk tier and a named human owner that Token Observe itself holds. So the expressible gates change. Deny this action because no role this agent holds names it. Refuse this call because the agent has spent its monthly ceiling, and refuse it before the tokens are billed rather than after the invoice. Park this exact refund on a named approver and hold the trace open until somebody decides. None of those are content judgements, and none of them require recognising anything about the prompt.
The honest reading is that the two see different things and neither substitutes for the other. WitnessAI’s context is rich about who the person is, what they appear to be doing, and where in a very wide estate they are doing it. Token Observe’s context is rich about what this specific agent was granted, what this call will cost, and what evidence the decision leaves behind — and it is blind to every interaction that never presents a token it issued, which in most organisations is the majority of AI use. A buyer running both is not buying twice for the same thing.
- Their decision point
- The network path, plus the endpoint reach of Witness/Anywhere for users who bypass it. Their product page’s claim is coverage without browser extensions or endpoint clients.
- Token Observe’s decision point
- Step 6 of eleven, before the payload leaves your network, returning one verdict — allow, block or require approval — plus a redaction plan, with the on-behalf-of intersection at step 6b where it is enabled.
- The order that is load-bearing
- Unicode sanitisation before any detector reads the string, detection before the verdict, and the human intersection after the verdict and before the approval branch, so a person is never asked to approve something the intersection forbids.
Where the two overlap most: refusing a tool call before it executes
This is the row worth reading twice, because both products now do it and the difference is not obvious from the marketing. WitnessAI’s Agentic Control post describes discovering agents across IDEs, applications, agent frameworks and custom agents on end-user devices or in the public cloud, mapping the MCP servers, tools and downstream systems each one reaches, scoring each server in an MCP Catalog against OWASP and CVE risk classes, and then having a security team approve which servers and tools agents may use. That approval is enforced organisation-wide, anything off the list is denied before the tool executes, a ban set organisation-wide cannot be quietly re-enabled or routed around by switching providers, and every blocked call writes an audit record naming the user, the agent, the tool and the rule that denied it. That is a coherent design and the discovery half of it is something Token Observe does not have.
Token Observe’s MCP gateway keys the same refusal to different facts. One Streamable HTTP endpoint sits in front of every registered upstream server; tools reach an agent namespaced and filtered to that agent’s grants; and — this is the part that matters — every call is re-authorised at execution independently of what the tool list showed, because filtering a list is a usability feature rather than access control and a client can simply guess a tool name. A tool the caller cannot see is answered as unknown rather than forbidden, since confirming that it exists is itself an inventory disclosure. Each tool’s name, description and input schema is hashed when an operator approves it and re-checked on every catalogue refresh, so an upstream that quietly rewrites a descriptor is quarantined and refused until a human approves it again — the answer to a rug-pull rather than to a prompt. And the result coming back is scanned as its own source and weighted higher than the same words typed by a person, on the reasoning that the author of a tool result is data, not a principal.
The limit belongs beside the capability and Token Observe’s own architecture document states it plainly: the product can only refuse a proposal it is shown. Tool calls the model merely proposes are evaluated on the way back as defence in depth, so a rule like refunds over £200 need approval binds even when the agent executes the tool in its own process — but an agent that routes nothing through the gateway is a shadow-AI radar problem if you have fed the radar, and nothing at all if you have not. WitnessAI’s answer to that same problem is the network position, which is why the two halves fit rather than collide.
- Their key
- An organisation-wide approved list of MCP servers and tools, scored against OWASP and CVE risk classes, with bans that cannot be re-enabled locally.
- Token Observe’s key
- This agent’s grants, re-checked at execution, plus the exact SHA-256 of the descriptor an operator approved — so drift quarantines the tool rather than flagging it.
- What neither claims
- Governing a tool call that reaches the resource through a path neither product is standing in. That is a coverage question, and it is the reason the radar reports rather than blocks.
The gates that bound an agent rather than classify it
Assume the classifier was right and the tool was on the list. What is left is the question of how much damage an agent that is behaving normally, or an agent that has been hijacked in a way nobody recognised, can actually do — and that is the part Token Observe is arguing for. Permissions are action-level and deny-by-default: an action no role names is refused, an explicit deny returns immediately before any allow is settled on so precedence never depends on the order roles happen to be listed in, and a delegation chain intersects rather than unions, so a low-privileged agent gains nothing by asking a higher-privileged one to act for it. The reason the intersection is the rule rather than a preference is recorded in the decision record: the chain arrives as a request header and is asserted rather than proven, so the only safe thing a forged chain can do is add links, and every added link must also allow.
Above that sit two gates that depend on recognising nothing at all. An approval carries the SHA-256 of the canonicalised action plus the execution context it was proposed in — this subject, with these grants, through this delegation chain, making this call with these arguments — is consumed by compare-and-set so two concurrent retries cannot both execute, and expires. And hard USD ceilings are reserved per request, per rolling hour, per UTC day and per UTC month in one per-agent transaction before egress, priced against the most expensive rate any provider or fallback in the resolved route could charge. A budgeted agent whose route has an unpriced reachable target is refused rather than priced at zero, because the product once shipped a price catalogue that loaded only under the demo seeder — so the documented production install had an empty price table, every trace recorded nothing, and every per-request ceiling admitted every request. The control was off while appearing to be on, and that is the defect the current design exists to prevent.
None of this is a claim to catch more than a detection vendor catches. It is the claim that a control which refuses because a named permission is absent, or because a named approver has not decided, or because a named ceiling would be crossed, is explainable to an auditor in a way a score is not — and that the resulting record is held by you rather than by a vendor, because the deployment is self-hosted and receives no telemetry. The cost of that posture is stated in the same breath: it is fail-closed, so its failures are your agents’ failures. A chain found corrupt at boot latches readiness and audit writes unavailable and later governed requests receive a 503, and there is deliberately no online clear, so recovery means restoring a database whose chain and independently retained head verify.
Where each one is the right answer
Choose WitnessAI when
- The question is what is my workforce doing with AI, across native applications, browsers, unmanaged devices and SaaS copilots — that estate is inside their published model and almost none of it presents a credential Token Observe issued.
- Detection quality is the requirement: intent classification, ten modelled risk dimensions, harmful-response filtering and a maintained sensitive-data model rather than nine published heuristics and eleven pattern classes.
- Your users work outside the corporate network and you need policy to follow them, which is exactly what Witness/Anywhere is described as solving through EDR, MDM, group policy, enterprise browsers or DHCP.
- Procurement needs a SOC 2 Type II report, a published price and a marketplace contract vehicle. Token Observe has none of those and will say so on the first call.
Choose Token Observe when
- The agents you are worried about hold credentials you issued, and the gap is the action rather than the content — what this one was allowed to do, who approved it, what it cost, and whether the ceiling refused it before the invoice.
- An approval has to be spendable exactly once against exactly one payload, so that a retry with one argument changed is refused as a mismatch rather than allowed as near enough.
- Self-hosted with zero vendor egress is a hard requirement, including air-gapped environments, and you would rather hold the audit chain, the MAC key and the anchor sink yourself than accept a hosted single-tenant instance that processes your prompts.
- You need the same policy set, redaction, budget and trace to apply identically across OpenAI, Anthropic, Gemini, OpenRouter, Bedrock and Azure OpenAI, with that equivalence enforced by a test rather than asserted.
When you would run both
Running both is the normal answer, and Token Observe’s own strategy makes it the expected one rather than a diplomatic concession: a generic AI firewall, prompt scanner or red-team platform is a named strategic non-goal, and the recorded instruction for this category — WitnessAI included by name — is to consume threat and identity verdicts from those systems rather than reproduce them. In that arrangement WitnessAI owns the estate: discovering the four thousand-odd AI applications people actually reach, following users who bypass the corporate network, classifying intent, filtering harmful responses, and enforcing an organisation-wide approved list of MCP servers and tools. Token Observe owns the agents that hold a credential it issued: the action-level permission set, the payload-bound approval, the hard USD ceiling reserved before egress, the multi-provider routing under one policy set, and a hash-chained audit log you can key and anchor off the box. The direction of integration is the part worth putting to a security reviewer, because it shortens the review: Token Observe holds no credential into your security stack, and consumes what you push to it on a credential it issued, so the worst a compromised deployment can do to your feeds is stop receiving them. The honest caveat is that this is a division of labour rather than a shipped integration — there is no WitnessAI connector in Token Observe today, the generic receiver shape would be the starting point, and anyone running both would be operating two policy sets and deciding which owns which rule.
The category argument sits above this one: Token Observe and ai security platforms covers what the whole category does and does not do, which is the better page to read if you have not yet shortlisted a product.
The others in the same slot
Keycard
Keycard decides whether the agent gets a credential. Token Observe decides the call and then proves what the call actually did.
Palo Alto Prisma AIRS
Prisma AIRS decides whether the content is malicious. Token Observe decides whether the agent that sent it was allowed to.
Cisco AI Defense
AI Defense attaches a policy to an application’s connection and inspects what crosses it. Token Observe attaches permissions to an agent and decides what it may do.
Zenity
Zenity gets into the path the agents are already on. Token Observe is the path the agents are pointed at.
Noma Security
Noma decides from what the agent appears to be doing. Token Observe decides from what the agent was allowed to do, before anything is scored.
Lakera
Lakera tells your application the content is an attack. Token Observe is the thing that refuses to send it.
Is Token Observe an alternative to WitnessAI?
Not for most of what WitnessAI is bought for. Their published product is governance of AI usage across an estate — network-level visibility without browser extensions or endpoint clients, detection of more than four thousand AI applications, endpoint reach for remote users through Witness/Anywhere, intent classification, seven guardrails and a four-action policy model. Token Observe governs agents that present a credential it issued and is blind to everything else, which its own compliance mapping lists under what the product does not evidence. Where the two genuinely overlap is refusing a tool call before it executes and attributing an agent action to a human, and even there they key the refusal to different facts. If shadow AI across a workforce is the problem, buy the product built for it.
What does Token Observe do that WitnessAI’s published material does not describe?
Three things, each with a limit attached. Payload-bound human approvals: a policy action that refuses with 403, mints an approval carrying the SHA-256 of the canonicalised action plus its execution context, consumes it once by compare-and-set and expires it — though nothing pushes that approval to the agent, which redeems it by retrying. Hard USD ceilings per request, rolling hour, UTC day and UTC month, reserved in one per-agent transaction before egress, with an unpriced reachable target refused rather than priced at zero — though a hard ceiling costs you one billable egress with no retry and no failover. And a self-hosted deployment in which the vendor receives no telemetry, prompts, keys or trace database at all. Whether any of those has since appeared in WitnessAI’s product is a question for WitnessAI; this page reflects what they published on 2 September 2026.
Both refuse a tool call before it executes. What is the actual difference?
What the refusal is keyed to. WitnessAI’s Agentic Control post describes an organisation-wide approved list of MCP servers and tools, scored in an MCP Catalog against OWASP and CVE risk classes, with anything off the list denied before the tool executes and a ban that cannot be quietly re-enabled or routed around by switching providers. Token Observe keys the refusal to this agent’s grants, re-checked at execution rather than inherited from what the tool list showed, plus the exact SHA-256 of the descriptor an operator approved — so an upstream server that rewrites a tool’s name, description or input schema has that tool quarantined until a human approves it again. Their discovery half is real and Token Observe has no equivalent; Token Observe’s descriptor pinning and per-agent scoping are not described in their published material as of 2 September 2026. Ask them about both, in writing.
Can Token Observe see the AI use WitnessAI sees?
No, and this is the single biggest asymmetry on the page. Token Observe governs traffic that presents a credential it issued: model calls through the OpenAI, Anthropic and Gemini dialects, and tool calls through its own MCP endpoint. Employees using a browser-based assistant, a native application such as Windows Copilot, a copilot embedded in a SaaS product, or a personal device on a home network are simply not in that path. What exists instead is a shadow-AI radar reconciling five evidence sources you feed it — vendor bills, network egress, service-account key audits, IDE and CLI telemetry, and Token Observe’s own caller and price consistency checks — into findings that belong in a risk register rather than into a block. Coverage travels with every surface that reports a clean result, because a dead feed must never be indistinguishable from a clean estate. WitnessAI’s network position is the answer to that problem; a radar finding is not.
Have you tested WitnessAI against Token Observe?
No. Every claim about WitnessAI on this page is a paraphrase of their own published material read on 2 September 2026 — the home, platform, Observe, Control, Protect, developer and applications pages, the policy-enforcement, Agentic Control, SOC 2, FinOps and cost-per-query posts, the Secure AI Enablement Platform whitepaper, the Witness/Anywhere solution brief and their AWS Marketplace listing — and none of it has been independently tested. There has been no witnessed comparison. Where a cell says a capability is not described in their published material, read that as an instruction to ask WitnessAI rather than as a finding: a capability that is merely undocumented reads identically to one that does not exist, and their product moves quickly. The same caveat runs in the other direction — Token Observe holds no SOC 2, no ISO certification and no independent penetration-test result, so a claim here about its behaviour is a claim you should verify in the software during the evaluation, which the licence is drafted to permit.
Prefer to ask a person? Write to us →
Tell us which one you are already running.
If WitnessAI is already in your stack, the useful question is not which to buy but what each is for, and where the seam between them sits. Say what you have and you will get a straight answer — including when the answer is that you do not need a second thing.
no form · no qualification step · no sales desk · the other three ways in