Token Observe vs 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.
2 September 2026
- Cisco DevNet — AI Defense Inspection API: Introduction
- Cisco DevNet — AI Defense Inspection API: Getting Started
- Cisco DevNet — AI Defense Inspection API: Authentication
- Cisco DevNet — AI Defense Management API: Introduction
- Cisco DevNet — AI Defense Management API: Authentication
- Cisco Blogs — Securing Enterprise AI: Cisco AI Defense Expands to Google Cloud
- Cisco Blogs — Security for the Agentic Era: Cisco AI Defense Breaks New Ground
- Cisco Blogs — Cisco AI Defense Gets Personal with Agent Security
- Cisco Blogs — Securing Agents and the AI Supply Chain with Cisco AI Defense
- Cisco Blogs — Cisco AI Defense and Armada: distributed AI
- Cisco Blogs — Cisco AI Defense: Explorer Edition
- Cisco Blogs — Cisco AI Defense: Built for the Way AI Is Actually Used
- Cisco Security Documentation Portal — AI Defense User Guide (contents read; article bodies require a portal login)
Their claims, not our testing. Verify anything that decides it for you.
On this page
For most readers shopping an AI security platform, Cisco AI Defense is the better purchase, and Token Observe’s roadmap says so before this page does
Detection, testing and supply chain are Cisco’s product and they are not Token Observe’s, and the gap is not narrow. Their published material describes an AI Bill of Materials that connects to repositories to create a consolidated inventory of AI assets and determine their provenance, an MCP Catalog extending that discovery to MCP servers across public and private registries, and scanning of model files, MCP servers and complete repositories for model backdoors, executable code and compromised tools; algorithmic red teaming with single and adaptive multi-turn testing for models and agents across hundreds of safety and security subcategories aligned with NIST, MITRE and OWASP, with the Management API describing validation reports adhering to MITRE ATLAS and the OWASP Top 10 for LLM Applications; and an Explorer Edition offering the same algorithmic red teaming as the Enterprise edition at no upfront cost across more than 200 risk subcategories. Against that, Token Observe ships nine weighted injection heuristics scored 1.25 times higher when the text arrived as a tool result, and eleven sensitive-data classes of which three are checksum-validated. Both are pattern matching rather than classifiers and both have false negatives, and the threat model names the role that must record a dated acceptance for each: the CISO for the heuristic injection detection, and the Data Protection Officer for the sensitive-data detection, whose recorded rationale is that this is a compensating control and not your only data-loss prevention.
Reach is the second advantage and it is the one that decides most estates. Cisco publishes three runtime enforcement points — an AI Defense Gateway, Multicloud Defense with AI Guardrails, and the Inspection API — plus an integration with Google Cloud’s Agent Gateway through GKE Service Extensions in which AI Defense operates as an inline policy enforcement engine on agent requests and responses, an ADK integration described as enabling runtime protection with a few lines of code, MCP runtime guardrails described as a sort of MCP gateway intercepting calls between an agent and an MCP server, and hybrid deployments in which sensitive AI payloads and runtime enforcement remain in the customer or tenant environment. Their User Guide contents list connectors for AWS, Azure, GCP and Cisco AI PODs alongside integrations for Splunk, Secure Access, Multicloud Defense and ServiceNow AI Control Tower. Token Observe has exactly one shape: a base-URL swap into a single self-hosted process, plus one Streamable HTTP endpoint for MCP. It sees what routes through it and nothing else, it has no network intercept, no browser control and no edge, and an agent that never presents a credential to it is a radar finding at best.
The third advantage is procurement, and it ends some evaluations on its own. Cisco is a public company with a trust posture, a support organisation and a paper trail your legal and security teams have probably processed before — ask them which certifications and independent test results cover AI Defense specifically, for what scope and to what date, because that is the only version of the answer worth having and this page does not hold it for them. Token Observe holds none: no SOC 2, no ISO 27001, no ISO 42001, no independent penetration test, no availability SLA, no published price list, and a licence that is a template pending review by counsel in England and Wales rather than an executed grant of rights. It is a single-writer SQLite process on one host at its current target scale, with no replica and no clustering, and it fails closed, so its own availability becomes a governance property of your environment. Every Cisco claim summarised above is taken from their own published material read on 2 September 2026, has not been independently tested, and there has been no witnessed bake-off between these two products; where a row below reads as an absence, treat it as a question to put to Cisco in writing rather than as a finding.
Token Observe and Cisco AI Defense, capability by capability
The Cisco AI Defense column paraphrases Cisco’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 each one sits
By an enforcement point you choose. Their Management API introduction states that to protect an application and its users with your policy you must set up a runtime enforcement point, which can be an AI Defense Gateway, Multicloud Defense with AI Guardrails, or the AI Defense Inspection API, and that you use the AI Defense web-based UI to set them up.
One shape only. For supported OpenAI-compatible, Anthropic and Gemini ingress, changing OPENAI_BASE_URL or ANTHROPIC_BASE_URL and one key is normally the whole integration, and eleven ordered steps then run in a single process on every governed request.
Their choice is an advantage for an estate with several traffic shapes; it also means the answer to “what happens on a block” depends on which point you picked.
Your application. Their Inspection API introduction states that when enforced by Inspection API calls, runtime protection does not rely on a gateway that intercepts traffic, that enforcement and decision-making remain within your application, and that this enables your application to allow or block each user prompt and model response based on the evaluation results.
Token Observe. It holds the payload at step 7, returns a typed error in the caller’s own dialect and closes the trace as blocked; the verdict is never handed back to the component being governed.
Both are inline. On their gateway and Multicloud Defense points Cisco stops the traffic itself; on the Inspection API point their own wording puts the enforcement in your code.
A connection. Their Management API introduction describes each chat application appearing as an Application, each Application including one or more Connections that represent the LLM APIs it protects, and a runtime protection policy applied to each connection to secure it.
An agent. Permissions are action-level and deny-by-default on a registry record the gateway resolves at step 2 of every request, so the same rule follows that agent across every provider it is routed to.
Described as an interception point in its own right. Their supply-chain post describes MCP runtime guardrails acting as a sort of MCP gateway, intercepting calls between an agent and an MCP server to combat threats like tool compromise, and examining interactions between the model and the MCP server; the agentic-era post adds a Tool Exploitation guardrail preventing adversaries from hijacking connected tools.
One Streamable HTTP endpoint in front of every registered upstream MCP server, governed by the same policy engine as a model call. Tools are namespaced server.tool, filtered to the agent’s grants and authorised again at execution, and each descriptor is hashed when an operator approves it so an upstream rewrite quarantines the tool.
Low, and stated as such. Their Google Cloud post describes bi-directional guardrails enforced inline across agentic workloads covering threats like prompt injection, tool misuse and data exfiltration with no code changes required, and an ADK integration letting teams enable runtime protection with a few lines of code.
One base URL and one key for model traffic, with no application refactor for supported ingress dialects, plus one endpoint for MCP. The cost is elsewhere: a fail-closed dependency in the request path that somebody has to own before it is switched on.
What each one enforces
The content. Their Management API introduction states that runtime protection secures LLM chat applications by inspecting user prompts and LLM responses in real time, and that when it detects content violating your security, privacy or safety policies it raises an alert in the Events log and, if configured, blocks the content from reaching the user or the LLM.
The authority. Deny-by-default action-level permissions on resources named model:gpt-5o-mini and tool:orderdb/get_details, where an explicit deny beats every allow wherever it is written and a delegation chain intersects at every hop rather than unioning.
These are not competing answers to one question. One asks whether the bytes are dangerous; the other asks whether this agent was allowed to send them at all.
In natural language, with an assistant. Their agent-security post introduces Policy Studio, describing the threat you want to protect against in natural language, uploading any organisational policy documents that might be relevant, and a Policy Studio agent asking follow-up questions to refine the policy; their User Guide contents list Policy Studio under Policies.
As a trigger, an action and a scope. Seven trigger kinds — tool call, model request, spend, rate, data class, injection score, time window — and five actions, resolved to one verdict in which a block beats an approval and an approval beats a redaction.
Not described in their published documentation as of 2026-09-02. Their material describes enforcement actions as blocked content or an alert about content, reported on the AI Events screen when a policy or rule is triggered.
Shadow mode on every rule: the match is recorded and the request proceeds, so you learn the false-positive rate before it stops real work. Where the deployment turns the gate on, no rule may begin enforcing until a backtest of that exact rule has been replayed against recorded traffic and acknowledged by a named person.
Only where no policy is attached. Their Inspection API authentication page states that once a policy is applied to a connection, individual API calls cannot override the policy using the enabled_rules parameter, and that enabled_rules may be specified only for connections that have no policy associated with them.
Never. The applicable policy set is selected from the agent’s id, team and tags; nothing in the request body selects which rules are evaluated, and a policy with an empty scope is global.
Not described in their published documentation as of 2026-09-02. Their product collateral on cisco.com — the data sheet, solution overview and product pages — refuses automated retrieval, so it was not read for this page; put the question to Cisco in writing rather than taking this row as an answer.
A 403 carrying the approval id, a status URL and a resume contract. The approval is bound to the SHA-256 of the canonical action plus its execution context, is single-use through a compare-and-set so two concurrent retries cannot both execute, and expires at 60 minutes by default and anywhere from one minute to seven days by policy.
Approving pushes nothing to the agent — Token Observe cannot call an agent back, so the agent redeems the approval by repeating the identical request with its id.
Denial-of-service appears in their published list of the threats runtime protection guards against, alongside prompt injection and data leakage. A per-agent USD budget is not described in their published documentation as of 2026-09-02.
Hard USD ceilings per request, per rolling hour, per UTC day and per UTC month, reserved before egress in one per-agent transaction, with a budgeted route whose reachable target is unpriced refused with a 409 rather than priced at zero. Above them a kill switch scoped to one agent, one team or everything, checked first in the pipeline.
By API key. Their authentication pages describe an Inspection API key generated per connection in the UI, carrying a name and either an expiry date or Never Expire, revocable and regenerable with the previous key deactivated immediately, presented in an X-Cisco-AI-Defense-API-Key header — and a separate tenant key for the Management API that cannot be used for the Inspection API.
By agent. A per-agent bearer token stored as a SHA-256 digest with a 16-character display prefix, shown once at issue, with revocation, expiry and last-used tracking — and the honest limit beside it is that any process holding the string is the agent, which is a stated residual risk rather than a solved problem.
What each one records
In Events. Their Management API introduction states that AI Defense produces events to alert you of runtime violations of your AI safety and security policies, retrievable through the Events endpoints, and their Inspection API introduction states that enforcement actions — blocked content, or an alert about content — are reported in the AI Events screen when a policy or rule is triggered.
In a trace. A trace id is minted at step 3, before the decision is taken, so a blocked request produces the same kind of record as one that succeeded, and the id is returned on every response.
Cisco documents the boundary themselves, which is worth knowing before you design around the API path. Their Management API introduction states that when you use the Inspection API to check compliance with a policy, violations are reported as Events and in the Inspection API response body; but when you use it to check compliance with a rule or rules, violations are returned only in the API response body and no event is generated.
The record does not depend on which surface asked. Every governed request writes a trace and its events, and every governance-plane change — an agent created, a policy widened, the kill switch engaged, an approval decided — is appended to the audit chain regardless of which API made it.
This row is why the two products compose cleanly on evidence: Cisco tells you exactly where their event stream stops, and that is a boundary rather than a defect.
Not described in their published documentation as of 2026-09-02.
A hash-chained audit log whose entry digest covers the previous hash plus the canonical JSON of that entry, so an edit breaks verification at a named sequence number. With an audit MAC key configured those digests become HMAC-SHA256 under a key held outside the database; the default is unkeyed, and an operator with write access can rewrite and recompute an unkeyed chain. Tamper-evident, not tamper-proof.
Documented. Their User Guide contents list a Splunk Integration, and, under the hybrid deployment guide, Log Export to SIEM via OpenTelemetry and Configure Observability for the Hybrid Connector.
Webhook events, an evidence export, and an OTLP over HTTP receiver that ingests bounded JSON and protobuf for logs, traces and metrics as an input rather than as a competing destination.
Their OpenTelemetry surface exports outwards and Token Observe’s ingests inwards, so a Splunk that already receives AI Defense logs is a plausible join between the two.
Not described in their published documentation as of 2026-09-02; events are retrieved through the Events endpoints of the Management API.
The traces and events for the period, the approvals with approver identity and rationale, the audit entries covering every governance-plane change, a chain verification result naming the sequence number of any break, and a SHA-256 digest of the bundle generated at a recorded time. The bundle is digest-sealed, not signed.
How each one deploys
Through Cisco Security Cloud Control. Their Getting Started page states that AI Defense is available through Security Cloud Control and that the base URL to use depends on where your AI Defense instance was created when claiming your subscription in Security Cloud Control.
You run it. One Node process and one SQLite file in your own network, bring-your-own-key, with a built-in mock provider so the whole thing can be seen working offline before any provider key exists.
Published per region. Their Getting Started page lists US (us-west-2), APAC (ap-ne-1) and Europe (eu-central-1) inspection base URLs, their agentic-era post cites availability across four global regions, and their User Guide contents include a Regional Points of Presence reference.
Wherever you deploy it, which is the same answer for one region or twenty — and the same limit, because there is no replica, no clustering and no distribution story.
Offered on named deployment shapes. Their Armada post states that sensitive AI payloads and runtime enforcement remain in the customer or tenant environment in supported hybrid deployments, and their Google Cloud post describes a VPC deployment option keeping all data within your Google Cloud environment with no external routing of prompts, responses or model interactions. Their User Guide carries a hybrid deployment guide with connectors for AWS, Azure, GCP and Cisco AI PODs.
The only shape there is. The vendor receives no product telemetry, no phone-home data, no prompts, no keys and no trace database, and the runtime data flow is documented so you can verify that rather than take it on assurance. Governed payloads leave your network only for the model and tool providers you configure, after policy and redaction.
Read the qualifier in Cisco’s own sentence: it is supported hybrid deployments, not every deployment, so confirm which shape your estate would actually be on.
Broad in their published description. Their post on how AI is actually used describes protecting every model and application the enterprise runs, regardless of vendor or deployment framework, and their User Guide contents include LLM API Provider Integrations, an AWS Bedrock integration and a Gemini Enterprise Agent Cloud Integration.
Six first-class upstreams — OpenAI, Anthropic, Google Gemini, OpenRouter, Amazon Bedrock and Azure OpenAI — plus any OpenAI-compatible endpoint you register, with the same permissions, policies, redaction, budgets and tracing applying identically, enforced by a table-driven parity test over every provider kind.
Cisco-operated for the SaaS surface, with a Cluster Availability Settings section in their hybrid deployment documentation. What Cisco commits to contractually is a question to put to Cisco in writing rather than to read off a comparison page.
A single-writer process on one host at this scale: no replica, no clustering, no vendor-operated uptime SLA, and fail-closed by design, so if it stops, governed agents cannot call models. Decide before you need to what happens when it is unavailable, and give that decision a named owner.
What each one costs
As a subscription claimed in Security Cloud Control. Their Getting Started page refers to claiming your subscription there, and their User Guide contents include Manage AI Defense subscriptions and a Subscriptions reference. A price is not described in their published documentation as of 2026-09-02.
A commercial source-available licence: use, modify and self-host, with redistribution and offering it as a competing hosted service not permitted. Security research and publication of results are expressly permitted.
Not described in their published documentation as of 2026-09-02; ask Cisco or a Cisco partner for a quote and for the entitlement metric it is priced on.
None published either. In a procurement that starts from a price list neither column helps you, and that is stated here rather than discovered on a call.
Explorer Edition. Their post describes a self-service offering with the same algorithmic red teaming capabilities as the Enterprise edition, at no upfront cost, evaluating models across more than 200 risk subcategories with support for major agentic frameworks, model providers and MCP-connected systems.
A 30-day evaluation written so a prospective customer’s security team can read, run and attack the software before a purchase order is raised, with no gag clause and no pre-approval of results — under a licence that is a template pending counsel rather than an executed grant.
Their free entry point is the red-teaming half of the product, which is precisely the half Token Observe does not have; the two are worth running in the same week rather than instead of each other.
Not described in their published documentation as of 2026-09-02.
You do, directly. Your providers invoice your own keys; Token Observe meters and prices each governed call, normalising cache tokens into mutually exclusive buckets before any arithmetic because providers disagree about whether cache reads sit inside the input total.
Ask Cisco which certifications and independent test results cover AI Defense, for what scope and to what date. Their published material aligns validation reporting to MITRE ATLAS and the OWASP Top 10 for LLM Applications, which is a testing framework rather than an audit of the service.
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 permitting your own pre-purchase test.
A policy on a connection asks whether the content is safe. A permission on an action asks whether this agent was allowed to send it
Cisco’s object model is published clearly enough to compare against, which is more than can be said for most of this category. A chat application appears as an Application. Each Application holds one or more Connections representing the LLM APIs it protects. You apply a runtime protection policy to each connection, and runtime protection then inspects user prompts and LLM responses in real time, raising an alert in the Events log and, if configured, blocking the content from reaching the user or the LLM. Rules live in the Policies section, enforcement actions are reported on the AI Events screen when a policy or rule triggers, and the whole thing can be driven from the UI or from the Management API. That is a coherent design for the question it answers, and the question it answers is whether the bytes crossing a connection are dangerous.
Token Observe starts from a different noun. The record it resolves at step 2 of every request is an agent: an id, a named human owner, a team, a declared purpose, a risk tier, a lifecycle status and a budget. Permissions hang off that record and they are action-level and deny-by-default, so a support agent may hold an allow on tool:orderdb/get_details while tool:payments/issue_refund is simply absent and therefore denied. An explicit deny beats every allow wherever it is written, so a narrow guardrail role cannot be outvoted by a broad grant. And when one agent delegates to another the chain intersects rather than unions, so a low-privileged agent gains nothing by asking a higher-privileged one to act for it — the failure mode of a forged delegation header is under-privilege rather than escalation.
The practical difference shows up on a request that contains nothing dangerous at all. A refund of £240 on a real order to the customer’s real account, proposed by an agent that has been talked into it by a paragraph of retrieved text, is clean content: no injected instruction survives into the tool arguments, no personal data is leaking, nothing in the payload would trip a content classifier. What is wrong with it is the authority. Either that agent should not hold tool:payments/issue_refund at all, or the amount crosses a threshold at which a named person has to agree to that exact payload before it happens. Both of those are decisions about the agent rather than about the string, and they are what Token Observe is for. The converse is just as true: an agent with impeccable permissions can still be handed a poisoned web page, and catching that is what Cisco is for.
Three enforcement points is a real advantage, and it changes what “blocked” means in the record
Cisco publishes the choice rather than hiding it, which makes it easy to evaluate honestly. To protect an application and its users with a policy you set up a runtime enforcement point, and it can be an AI Defense Gateway, Multicloud Defense with AI Guardrails, or the Inspection API, configured through the web UI. On the Google Cloud path they describe integrating with the Agent Gateway through GKE Service Extensions and operating as an inline policy enforcement engine on agent requests and responses. For an organisation whose AI traffic arrives in several different shapes — some through a gateway you control, some through cloud-native runtimes, some inside applications your own developers ship — being able to pick the point per shape is worth a great deal, and Token Observe has no equivalent flexibility to offer.
The Inspection API path is the one to read carefully, because Cisco describes its consequence precisely and it is the sort of detail an architecture review should catch early. Runtime protection on that path does not rely on a gateway that intercepts traffic; the application sends prompts and responses to the Chat Inspection or HTTP Inspection endpoint, and enforcement and decision-making remain within the application, which allows or blocks based on the evaluation results. So the guardrail returns a judgement and the component being governed acts on it. That is exactly the right design when the application is yours and you want the decision in your own code, and it is a different security property from a control that holds the payload — because the thing honouring the verdict is the thing the verdict is about, and a bug, a caught exception or a developer in a hurry sits between the judgement and the outcome.
Token Observe only has the one shape, and it accepts the cost of that. It is in the path, it holds the payload, and a block is a typed error it returns to the caller in the caller’s own dialect while the trace closes as blocked. On a streamed response there is no status line left to spend, so a blocking data class ends the stream with an in-band ACP_POLICY_BLOCKED frame the instant that class is seen, and the outbound stream passes through a hold-back buffer with a 64-character floor so a card number split across two chunks cannot escape masking. The price of being the only enforcement point is that Token Observe’s own availability becomes a governance property of the estate: it fails closed, and if it stops, governed agents cannot call models. That is stated as a limit rather than sold as a feature, and a design-partner gate does not pass until the emergency decision has a named owner.
- Where their record follows the choice
- Cisco states that Inspection API checks against a policy are reported as Events and in the response body, while checks against a rule or rules return violations only in the response body and generate no event. If your evidence plan depends on the Events stream, that distinction decides how you call the API.
- Where Token Observe’s record does not follow anything
- The trace id is minted at step 3, before the verdict, so a refusal is recorded on the same terms as a success. There is no configuration under which a governed request is decided and not written down; if the audit layer cannot write, governed requests receive a 503 rather than proceeding unrecorded.
- The tool-call seam both products care about
- Cisco describes MCP runtime guardrails intercepting calls between an agent and an MCP server. Token Observe evaluates a tool call the model proposes on the way back, at step 10, so a rule about refunds over a threshold binds even when the agent executes the tool itself — but it can only refuse a proposal it is shown, and an agent that routes its tools nowhere near either product is governed by neither.
What running both actually looks like, and the connector that does not exist yet
Start from the honest position, which the roadmap states rather than the marketing: a generic AI firewall, prompt scanner or red-team platform is a named strategic non-goal, and the recorded instruction for this category is to consume threat and identity verdicts from products like Cisco AI Defense rather than reproduce them. That is not diplomacy. Nine weighted heuristics and eleven data classes against a maintained detection product with red teaming behind it is not a contest, and a page that pretended otherwise would be arguing with its own strategy document. So the arrangement worth designing is one where Cisco decides what the content is and Token Observe decides what the agent may do with it.
The join available today is the log stream rather than a verdict API, and it is worth describing exactly so nobody plans around something that is not there. Cisco’s User Guide contents list a Splunk Integration, and, for hybrid deployments, log export to a SIEM over OpenTelemetry. Token Observe’s shadow-AI radar ships receivers for six vendor-shaped feeds — Cloudflare Logpush, Palo Alto Strata Logging Service, Zscaler, Netskope, Elastic and Splunk — plus a generic shape that reads its own field names for any tool that can export on a timer, and separately runs an OTLP over HTTP receiver for logs, traces and metrics. If your AI Defense events already land in Splunk, that is the path with the least new plumbing in it. The direction matters for the security review: you configure an outbound feed in a console you already administer, delivering on a credential Token Observe issued, so Token Observe holds no credential into your security stack and the worst a compromised deployment can do to it is stop receiving.
What does not exist is a purpose-built Cisco AI Defense connector, and there is no point implying otherwise. There is no integration today in which an AI Defense verdict becomes a first-class policy trigger inside Token Observe, and building one is a roadmap intention rather than shipped code. Until then, running both means operating two policy sets and deciding deliberately which owns which rule: content safety, injection detection, model and MCP supply-chain scanning and red teaming on the Cisco side; who the agent is, what actions it holds, what it may spend, which payloads need a person, and the hash-chained record of all of it on the other. Two inline components in one request path is also a second failure domain and a second hop of latency, so the usual starting shape is narrow — route only the agents that take consequential actions through Token Observe, and leave the rest where they are.
- What Cisco should stay authoritative for
- Discovery and inventory across the estate, model and repository scanning, MCP supply-chain scanning, red teaming before deployment, and content-level runtime guardrails at whichever enforcement points your traffic shapes need. None of that is on Token Observe’s roadmap and none of it is coming.
- What Token Observe adds beside it
- Deny-by-default action-level permissions, an approval bound to one exact payload and spendable once, hard USD ceilings reserved before egress, a kill switch checked first in the pipeline, and an audit chain you can anchor with Ed25519 to a sink outside your database administrator’s control.
- The limit that survives running both
- Neither product sees an agent that presents a credential to neither. Token Observe’s answer is a radar built on evidence you send it, and every clean result carries a coverage report, because an empty findings list is the same bytes whether nobody is bypassing the gateway or the export died in July.
Where each one is the right answer
Choose Cisco AI Defense when
- The requirement is detection quality — prompt injection, tool exploitation, data exfiltration and content safety judged by a maintained product rather than by nine published heuristics with a written residual-risk acceptance behind them.
- You need what happens before deployment: model and repository scanning, an MCP catalogue and bill of materials, and red teaming across hundreds of subcategories aligned to NIST, MITRE and OWASP.
- Your AI traffic arrives in several shapes and you want to pick an enforcement point per shape, including cloud-native runtimes and applications where the decision should stay inside your own code.
- Coverage of the whole estate matters more than depth on the agents you have registered, because most of the exposure is in places no gateway credential is ever presented.
- Procurement needs a vendor with an established assurance posture, a support organisation and paperwork your legal team has seen before. Token Observe has none of that and says so on the first call.
Choose Token Observe when
- The gap you have found is the action rather than the content: what the agent was allowed to do, who approved it, what it cost, and whether the thing authorised is the thing that happened.
- A human decision has to be bound to one exact payload, single-use and expiring, with the approver and their rationale recorded against the trace — not to an action type that a retry with one changed argument still satisfies.
- You run more than one model provider and the same rule has to fire identically on all of them, with the equivalence tested over every provider kind rather than asserted.
- You need a hard USD ceiling and a kill switch that are enforced before egress rather than reported afterwards, because the failure you are guarding against is an agent that works exactly as designed and does it four thousand times.
- Self-hosted with zero vendor egress is a requirement rather than a preference, including air-gapped environments, and the evidence has to be a hash-chained record you can verify with a script that holds no database and no network.
When you would run both
Running both is the normal answer and the product’s own roadmap makes it the expected one: a generic AI firewall, prompt scanner or red-team platform is a named strategic non-goal, with a recorded instruction to consume threat verdicts from products like Cisco AI Defense rather than reproduce them. In that arrangement Cisco keeps everything it is genuinely better at — discovery and the central AI inventory, the bill of materials and MCP catalogue, model, repository and MCP supply-chain scanning, algorithmic red teaming before anything ships, Policy Studio for authoring content guardrails from a natural-language description and your own policy documents, and runtime content guardrails at whichever of their three enforcement points suits each traffic shape. Token Observe takes the layer underneath the content question: which agent this is, which actions it holds, what it may spend per hour and per month, which payloads stop for a named human, and a hash-chained record of every governed request and every governance-plane change that an auditor can read and an offline script can verify. The join available today is your log stream rather than an API — Cisco documents a Splunk integration and OpenTelemetry log export from hybrid deployments, and Token Observe’s radar already reads Splunk-shaped deliveries while its own OTLP receiver ingests logs, traces and metrics — and the direction of that feed is deliberate, because Token Observe holds no credential into your security stack. Three caveats belong beside the recommendation rather than after it: there is no purpose-built Cisco AI Defense connector today and an AI Defense verdict is not yet a first-class policy trigger; two inline components in one request path is a second failure domain and a second hop of latency, so start with only the agents that take consequential actions; and every Cisco claim on this page is vendor-authored, read on 2 September 2026, and untested by anyone here.
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.
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.
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.
Is Token Observe an alternative to Cisco AI Defense?
Not for most of what AI Defense does. Their published material covers AI asset discovery and inventory, an AI bill of materials and MCP catalogue, model and repository scanning, algorithmic red teaming across hundreds of subcategories, and content guardrails at three runtime enforcement points; Token Observe does none of the first four and has no plans to, because a generic AI firewall, prompt scanner or red-team platform is one of its seven named strategic non-goals. The overlap is one layer: both sit inline on model and tool traffic and both can stop a request. Where they differ is what the verdict is about — Cisco’s is about the content crossing a connection, Token Observe’s is about whether the agent held the authority to make that call, what it costs, and whether a named person agreed to that exact payload.
Both products are inline. What is the actual difference in the request path?
Who holds the payload when the verdict is taken. On Cisco’s gateway and Multicloud Defense enforcement points the traffic is intercepted and stopped there. On their Inspection API point, their own documentation states that runtime protection does not rely on a gateway that intercepts traffic, that enforcement and decision-making remain within your application, and that your application allows or blocks each prompt and response based on the evaluation results — a good design when the code is yours, and a different property from a control that holds the bytes. Token Observe only ever has the second shape: eleven ordered steps run in one process, the single decision point is step 6, and a block is a typed error returned in the caller’s own dialect with the trace closed as blocked. The cost of that is stated plainly: it fails closed, so if it is down, governed agents cannot call models.
We already run AI Defense. What would Token Observe add?
The deterministic layer that bounds what a missed detection can do. Action-level permissions that are deny-by-default, so an action no role names is refused and a delegation chain intersects rather than unions. An approval bound to the SHA-256 of the canonical action plus its execution context, single-use through a compare-and-set and expiring in an hour by default, so a retry with one argument changed is a mismatch rather than a near-enough. Hard USD ceilings per request, hour, UTC day and UTC month, reserved before egress. A kill switch scoped to one agent, one team or everything. And a hash-chained audit log with an optional Ed25519 anchor published to a sink outside your database administrator’s control, plus an export carrying the chain verdict — tamper-evident rather than tamper-proof, and digest-sealed rather than signed. None of those depend on recognising an attack.
Have you tested Cisco AI Defense against Token Observe?
No. Every claim about Cisco on this page comes from their own published material — their DevNet documentation for the Inspection and Management APIs, several posts on their engineering blog, and the contents of their AI Defense User Guide — read on 2 September 2026 and not independently verified. There has been no witnessed bake-off, and one is named in the product’s own launch gates as evidence that does not yet exist. Where a cell says a capability is not described in their published documentation, read that literally: it means it was not in the pages read on that date, not that Cisco does not have it, and the accurate account of a product this size is the one you get from the vendor in writing. Note also that the bodies of their User Guide articles sit behind a portal login, so that source is cited for its contents rather than for detailed article text.
Which one should we buy if we can only buy one?
If nobody has yet asked you what your agents are allowed to do, buy Cisco. Detection, red teaming, supply-chain scanning and coverage of the parts of your estate that never present a gateway credential are their product and are not Token Observe’s, and they arrive from a vendor whose assurance posture and support model your organisation can already process — Token Observe holds no SOC 2, no ISO 27001, no ISO 42001 and no independent penetration test, publishes no price list, offers no availability SLA, and its licence is a template pending review by counsel rather than an executed grant. The case for the other column starts when an agent can issue a refund, merge a pull request, send an email or write a row, and someone has to prove afterwards which agent was permitted to do it, who agreed to that exact payload, what it cost and that the record has not been edited since. That is a question about authority and evidence rather than about content, and it is the only question this page claims Token Observe answers better.
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