Token Observe vs 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.
2 September 2026
- Noma Security — homepage
- Noma — platform overview
- Noma — AI-DR runtime protection
- Noma — AI security posture management (AI-SPM)
- Noma — security solution for AI agents
- Noma — endpoint agent security for Claude Code, Cursor and Codex
- Noma — AI application security for Bedrock, Azure and Databricks
- Noma blog — controlling agent and MCP access with Noma
- Noma blog — launching agentic access control
Their claims, not our testing. Verify anything that decides it for you.
On this page
On coverage, on detection and on the assurance a procurement team asks for, Noma is the better purchase for most readers
Start with the estate, because it decides more purchases than the feature list does. Noma’s published platform spans four named products — AI-SPM, Access Control, AI Red Teaming and AI-DR — described as discovering, governing, testing and protecting AI and agents across the enterprise, and the discovery half alone covers ground Token Observe has no way to reach. Their AI-SPM page describes finding every agent, MCP server, toolset, skill and model across endpoint AI, SaaS and homegrown agents, and states that organisations discover ten to a hundred times more agents than teams expect. Their platform names Claude, Gemini, Bedrock, Azure, Databricks, LangChain, CrewAI, Copilot Studio, AgentForce, ServiceNow and MCP servers among what it covers; their endpoint page names Claude Code, Cursor and Codex on developer machines, discovered agentlessly through your existing EDR or MDM with no new endpoint agent to deploy. Token Observe governs what presents a credential to its gateway and nothing else, and its own endpoint hook for managed developer subscriptions is explicitly a preview that reports itself as not production-eligible. If the question you are answering is which agents exist in this company and what can they reach, that is Noma’s product and it is not Token Observe’s.
Detection quality is the second advantage, and Token Observe’s own documentation concedes it rather than arguing. Noma publishes a runtime context engine reading the full behavioural chain of every agent session — prompts, tool calls, data access and actions taken — evaluated against the session, the identity behind the agent, the data involved and behaviour over time, with detectors independently tunable to monitor, alert, block or mask, hundreds of out-of-the-box policies, benchmarks and protection profiles, and custom detection logic authored in natural language. Alongside it they publish AI Red Teaming that tests applications with adaptive multi-turn campaigns escalating pressure at each turn, and compliance presets mapping findings to NIST AI RMF, the EU AI Act, ISO 42001, the OWASP LLM Top 10 and MITRE ATLAS. What Token Observe ships against that is nine weighted injection patterns scored 1.25 times higher when the text arrived as a tool result, and eleven sensitive-data classes of which three are checksum-validated. It is not a classifier, the injection heuristic has false negatives the register requires the CISO to accept in writing, and free-text personal data is not detected at all — a separate residual naming the Data Protection Officer as the required acceptor. Against a maintained detection product, a nine-pattern heuristic ought to lose, and it does.
Then there is procurement, which decides real deals and is worth stating plainly rather than in a footnote. Noma’s homepage displays HIPAA, SOC 2, ISO 27001 and ISO 9001:2015; ask them which of those are current, 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 of it: no SOC 2, no ISO 27001, no ISO 42001, no independent penetration-test result, no availability SLA, no published price list, and a licence file that is a template pending review by counsel rather than an executed grant. If a certification is your gate, the comparison ends here in Noma’s favour and there is no argument to make. Every description of Noma above is taken from their own public, vendor-authored pages read on 2 September 2026 and has not been independently tested; where a cell below says a capability is not in their published documentation, read it as a question to put to them in writing rather than as a finding, because a capability that is merely undocumented reads identically to one that does not exist.
Token Observe and Noma Security, capability by capability
The Noma Security column paraphrases Noma Security’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
Attached to control points you already run. Their homepage describes Open Enforcement, which “decouples policy from infrastructure: define governance once, and Noma enforces it at every agent control point you already run, through agent hooks, MCP gateways, AI gateways, agent SDKs, and direct APIs”; their endpoint page describes organisation-level agent hooks, gateways or APIs covering desktop, CLI and cloud sessions.
The control point itself. Traffic passes through one process and eleven ordered steps run in it — authenticate, resolve agent, open trace, sanitise, scan, govern, enact, route, call upstream, govern the response, meter and record.
Both enforce inline; neither is out of band. The difference is that Noma attaches to enforcement points you already operate, while Token Observe is a new one you put in the path.
Through what is already there. Their endpoint page describes agentless discovery using “your existing EDR or MDM, with no new endpoint agent to deploy”, and their homegrown-agents page describes connecting “through APIs to your cloud providers, data platforms, model registries, version control, and notebooks”.
One environment variable. For supported OpenAI-compatible, Anthropic and Gemini ingress, changing OPENAI_BASE_URL or ANTHROPIC_BASE_URL and one key is normally the whole integration, plus one Streamable HTTP endpoint for MCP.
Theirs is the lower-friction integration and their pages say so; the cost of Token Observe’s is that it is a fail-closed component in the path of every governed model call.
Behaviour in context. Their runtime page describes a Runtime Context Engine inspecting agent behaviour across “event, session, identity, data, and baseline” layers, and their endpoint page describes “the full behavioral chain of every agent session (prompts, tool calls, data access, actions)”.
The payload itself, in a fixed order: Unicode sanitisation first, then eleven sensitive-data classes of which three are checksum-validated, then nine weighted injection heuristics scored 1.25× higher when the text arrived as a tool result.
Sanitisation runs before detection deliberately: a scanner reading un-normalised text is looking at a different document from the one the model will read, which is the whole ASCII-smuggling attack.
Estates rather than provider APIs. Their platform page names Claude, Gemini, Bedrock, Azure, Databricks, LangChain, CrewAI, Copilot Studio, AgentForce, ServiceNow and MCP servers; their homegrown-agents page names AWS Bedrock, Azure AI Foundry and Databricks.
Six first-class upstreams — OpenAI, Anthropic, Gemini, OpenRouter, Bedrock and Azure OpenAI — under identical policy, redaction, budgets and tracing, with that equivalence enforced by a table-driven parity test over every provider kind.
The two lists are not the same kind of list. Theirs describes environments the platform covers; this describes provider dialects the gateway terminates, prices and routes between.
Tool-level, with a three-state registry. Their access-control post argues that “a ‘Read-Only’ MCP tool carries far less risk than one with write or delete capabilities, but both can exist within the same MCP server”, contrasting the GitHub server’s issue_read with its delete_file and create_pull_request, and describes resources as approved, requires review, or blocked — where “after it’s been blocked once, it is blocked everywhere, automatically”.
One endpoint in front of every registered upstream server. Tools reach an agent namespaced and filtered to its grants, every call is re-authorised at execution rather than trusted from the listing, and each descriptor is hashed at approval so an upstream rewrite quarantines the tool until a human approves it again.
Found by discovery. Their AI-SPM page describes finding “every agent, MCP server, toolset, skill, and model, across all agent types: endpoint AI, SaaS, and homegrown”, and states organisations discover ten to a hundred times more agents than expected.
Not governed. A shadow-AI radar reconciles five evidence sources — vendor bills, network egress, service-account key audits, IDE and CLI telemetry, and Token Observe’s own caller and price consistency checks — into findings for a risk register, and chasing those agents down inside your organisation is your work.
This is the largest gap on the page and it runs in Noma’s favour. Absence of evidence is not evidence of absence, and Token Observe’s own compliance mapping lists ungoverned agents under what it does not evidence.
What it enforces
Their runtime page states detectors are “independently tunable for sensitivity and action: monitor, alert, block, or mask”, and their homegrown-agents page states that based on policy it “alerts, blocks, masks data, or routes to a human”.
Five policy actions resolved to one verdict — block, require approval, redact, warn, suspend the agent — with a fixed precedence in which a block beats an approval and an approval beats a redaction.
Detection logic. Their runtime page describes “hundreds of out-of-the-box policies, benchmarks, and protection profiles” alongside AI Guardrails that “let you define custom detection logic in natural language”.
Seven typed trigger kinds: the tool being called and its argument values, the model requested and its estimated input size, accumulated spend, request and token rate, detected data classes, the injection score and the source the text came from, and the hour of day in UTC.
Different registers rather than better and worse. Prose detection logic expresses things a typed condition cannot; a typed condition can trigger on a number of dollars.
Identity-keyed and tool-scoped. Their access-control launch post says that “rather than operating under shared credentials or permissive service accounts, every agent’s actions trace back to a specific identity”, and that you can “apply those policies at the granularity of tool, agent type, user, team, or environment”; their endpoint page says policies are “keyed to IdP groups and users, or applied organization-wide based on tool sensitivity”.
Deny-by-default and action-level. An action no role names is refused, an explicit deny beats every allow wherever it is written, and a delegation chain intersects rather than unions, so a low-privileged agent gains nothing by routing work through a higher-privileged one.
A response and a queue. Their homegrown-agents page lists “routes to a human” among the responses policy can take, and their access-control post describes an item that “surfaces automatically in the review queue with its full risk profile”. How an approval is bound to a specific action, whether it can be replayed, and whether it expires are not described in their published documentation as of 2 September 2026.
Bound to the SHA-256 of the canonicalised action plus the execution context it was proposed in, single-use through a compare-and-set so two concurrent retries cannot both execute, and expiring — 60 minutes by default, one minute to seven days by policy. Change one argument and the hash no longer matches, so the retry is refused as a mismatch rather than allowed as near enough.
USD budgets, token ceilings and request rate limits are not described in their published documentation as of 2 September 2026. Ask them whether spend is a control or a report.
Hard USD ceilings per request, per rolling hour, per UTC day and per UTC month, reserved before egress in a single per-agent transaction, alongside requests, tool calls and tokens per minute. A budgeted agent whose resolved route or fallback cannot be priced is refused with a 409 before egress rather than priced at zero.
The failure this guards against is specific: an empty price table once meant every trace recorded $0 and every ceiling admitted every request — the control off while appearing to be on.
Their runtime page describes monitor as one of the actions a detector can be tuned to, alongside alert, block and mask.
Every rule can run in shadow mode first, recording what it would have done without stopping anything. 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.
Their access-control post says every agent and MCP connection is checked against the registry the moment it is made, and that “blocked ones are prevented from connecting”; blocking also propagates, because “after it’s been blocked once, it is blocked everywhere, automatically”.
A kill switch scoped to one agent, one team or the whole estate, checked first in the pipeline — ahead of lifecycle status, permissions, budgets and policy — so it reaches even the routes that execute nothing.
What it records
Posture and audit-ready reporting. Their AI-SPM page describes generating “compliance reports with evidence for auditors and board-level reporting”, and mapping findings to NIST AI RMF, the EU AI Act, ISO 42001, the OWASP LLM Top 10 and MITRE ATLAS.
Evidence about one request. The flight recorder holds the post-redaction prompt excerpt, the tool calls and their arguments, the policy decisions, the human approvals, the tokens and the cost, in a timeline that explains each step in a plain sentence rather than a log line.
These answer different questions. Theirs answers how exposed is this estate against a framework; this answers what did this agent do at 14:32 and who let it.
Their runtime page lists “audit trails” among the compliance evidence the platform produces, alongside inventory, framework mappings and test results. The construction behind them — whether entries are chained, keyed, signed or externally anchored — is not described in their published documentation as of 2 September 2026. Ask them what it is.
A hash chain in which each entry’s digest covers the previous entry’s hash plus the canonical JSON of its own content, so an edit or a deletion breaks verification at a named sequence number. It is tamper-evident, not tamper-proof, and the default is unkeyed SHA-256 that an operator with write access can rewrite and recompute — the repository ships a forgery test asserting exactly that.
Three layers, weakest named first: unkeyed SHA-256 by default, HMAC-SHA256 under a MAC key held outside the database, and periodic Ed25519 anchoring published off the box. Every verification result and every export reports which of the three you are holding.
Their AI-SPM page describes “mapping the blast radius of each agent, surfacing the toxic risk combinations that actually matter”, and their platform page describes tracking the “full chain of agent actions across an entire session”.
A compliance officer’s question in English is translated into a validated filter object over fourteen allow-listed fields — never into SQL, because trace content is attacker-influenced by construction — and the interpreted filter comes back as editable chips. It degrades to a deterministic keyword parser when no model answers, and it cannot group, count or correlate across traces.
Their AI-SPM page describes compliance reports with evidence for auditors and board-level reporting, with findings mapped to the frameworks named above.
An export sealed with a SHA-256 digest over canonical JSON and carrying the audit chain’s verdict. It is digest-sealed and not signed; durable origin evidence comes from the keyed chain plus an Ed25519 anchor retained independently of the database.
Retention periods for the telemetry the platform holds are not described in the pages read on 2 September 2026. Ask for them in writing alongside the data-processing terms.
Trace retention is unset by default, and unset means keep forever. That is a decision left to you rather than made for you, and it is the wrong default for anyone who has not made it deliberately.
How it deploys
Their homepage describes deployment across three scenarios — endpoint AI agents, SaaS agents and homegrown agents — and their endpoint page describes agentless discovery through “your existing EDR or MDM”. A self-hosted, customer-operated or air-gapped deployment is not described in the pages read on 2 September 2026.
Self-hosted only. One Node process, one SQLite file, on a host you run; there is no vendor-hosted tier to choose instead, which is a constraint as often as it is a feature.
Their homegrown-agents page describes connecting through APIs to your cloud providers, data platforms, model registries, version control and notebooks, “building a continuous inventory of every agent, model, MCP server, and skill”. What is transmitted to the platform and where it is retained is not described in the pages read on 2 September 2026.
Governed payloads leave only for the model and tool providers you configure, after policy and redaction. 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 a reviewer can verify that rather than take it on assurance.
Their homepage displays HIPAA, SOC 2, ISO 27001 and ISO 9001:2015. Ask Noma which are current, for what scope and to what date — that is the only version of the answer worth having, and this page does not hold it for them.
None. No SOC 2, no ISO 27001, no ISO 42001 and no independent penetration test. Stated on the first call rather than under questioning, and if a certification is your gate the comparison ends on this row.
Their homepage describes Open Enforcement across control points you already run, including agent hooks, MCP gateways and AI gateways. What each enforcement point does when the platform cannot be reached is not described in the pages read on 2 September 2026, and it is worth asking about every point separately.
Fail-closed and in the path: if Token Observe is down, governed agents cannot call models, and there is no replica, no clustering and no vendor uptime commitment at its current target scale. The endpoint hook is built the opposite way for the opposite reason — it decides locally against an Ed25519-signed policy bundle and makes no network call at all, because every vendor hook fails open on timeout and a hook that round-tripped would turn each slow VPN into a silent org-wide bypass.
What it costs
No price is published on any of the pages read on 2 September 2026. Ask for the licensing unit before the number, because whether it counts agents, seats, environments or protected assets decides the shape of the bill more than the rate does.
No published price list either. The commercial arrangement is discussed rather than looked up, which is a real disadvantage in a procurement process that starts with a budget line.
There is nothing to compare on this row. Both vendors should be asked the same question, and the answer should be in writing.
One platform spanning four named products — AI-SPM, Access Control, AI Red Teaming and AI-DR — described on their homepage as discovering, governing, testing and protecting AI and agents across the enterprise.
One component with a narrow surface. Its optional modules default off — audit anchoring, policy backtesting, on-behalf-of intersection and radar scheduling each require an operator to switch them on — so an upgrade changes no behaviour until somebody decides it should.
Licensing terms are not published on the pages read on 2 September 2026.
Commercial source-available: use, modify and self-host under a licence, with redistribution and offering it as a competing hosted service excluded, and security research and publication of results expressly permitted. The licence file is a template pending review by counsel rather than an executed grant.
Support tiers and service-level commitments are not described in the pages read on 2 September 2026.
A published support model with severity definitions and response targets, and a plain statement of why there is no availability SLA yet: the vendor does not operate your deployment and has no telemetry from it, so an uptime number from a party with access to neither would be unmeasurable by both sides.
Their AI Red Teaming product tests your own applications — their platform page describes a red team that behaves “like a real attacker, compounding techniques into multi-turn campaigns that escalate pressure at each turn”. How an evaluation of the platform itself is structured is a question for them.
A 30-day evaluation written so your security team may read, run and attack the software before a purchase order is raised, with no gag clause and no pre-approval of results — under the template licence above, alongside a published defect list and a published residual-risk register.
Detection and authority are two halves of one control, and this page only argues about the second
Noma’s published verdict comes from behaviour. Their runtime engine evaluates each action in context — the content of the event, the session before it, who the agent acts for, the data in reach, and behaviour baselined over time — and then alerts, blocks, masks data or routes it to a human, with detection running across the full chain of actions in a session rather than on isolated requests. That shape catches the thing a permission set cannot anticipate: an agent whose every individual call is permitted but whose sequence of calls is an exfiltration, or a prompt injection phrased in a way nobody wrote a rule for.
Token Observe’s verdict comes from authority, and it is decided before any scoring happens. Permissions are action-level and deny-by-default, so an action no role names is refused whether or not anything looked suspicious; an explicit deny beats every allow wherever it is written; a delegation chain intersects rather than unions, so agent A gains nothing by asking higher-privileged agent B to do what A was just refused. Above that sit gates that do not depend on recognising anything at all — a payload-bound single-use approval, a hard USD ceiling reserved before egress, a kill switch checked first in the pipeline. None of those need to identify an attack in order to stop one.
The reason to say this plainly is that the two failure modes are opposite, and a buyer choosing only one inherits the other. Detection alone fails open on the novel case: a phrasing nobody has seen scores below threshold and the action proceeds with whatever authority the agent already held. Authority alone fails on the permitted-but-wrong case: an agent doing exactly what it was allowed to do, at exactly the wrong moment, for a reason a paragraph of retrieved text supplied. Token Observe’s own material is explicit that its detection is a compensating control and not the customer’s only DLP, and that heuristic injection scoring carries false negatives requiring the CISO’s dated written acceptance. That is not a preamble to an argument; it is the argument for running a detection platform beside it.
- Their decision input
- The event, the session around it, the identity the agent acts for, the data in reach and behaviour baselined over time, with detectors independently tunable to monitor, alert, block or mask.
- This product’s decision input
- Seven typed triggers over one request — tool and argument values, model and estimated input size, accumulated spend, request and token rate, detected data classes, injection score and source, and the UTC hour.
- Where they meet
- An injection score is one of the seven triggers rather than the control, which is exactly the slot a better detector belongs in. A verdict from a maintained platform is more useful in that slot than nine patterns are.
What bounds an injection that both products missed
Assume the detection missed it, because sometimes it will, and assume the permission set allowed it, because the agent needed that permission to do its job. What is left is the set of controls that do not depend on recognising anything, and this is the part of Token Observe worth putting beside a detection platform rather than instead of one.
An approval is bound to the SHA-256 of the canonicalised action plus the execution context it was proposed in. That binding is the whole control: an approval that authorises a refund rather than this refund of £240 on this order to this account is a standing licence for every refund the agent proposes afterwards, and the agent proposing them is the component most likely to have been talked into it. Consumption is a compare-and-set, so two concurrent retries cannot both execute, and the record expires — 60 minutes by default, one minute to seven days by policy. Approving pushes nothing to the agent, because Token Observe has no way to call an agent back; the agent redeems the approval by repeating the identical request with its id, once.
Money is the other gate that needs no recognition. Ceilings in USD apply per request, per rolling hour, per UTC day and per UTC month, and the money verdict is deliberately taken last: permissions, rate limits and policy resolve first, the route is resolved, then every provider and fallback that route could execute is priced and the most expensive of those rates is reserved against the agent’s windows inside a single per-agent transaction. A budgeted route whose target cannot be priced is refused with a 409 before egress rather than admitted at zero. The cost of a hard ceiling is stated beside it: one billable egress, no retry and no failover, because a ceiling that may be exceeded by a retry is not a ceiling.
The honest boundary on all of it is that Token Observe can only refuse a proposal it is shown. Tool calls the model proposes are re-evaluated on the way back, which is why a rule about refunds over a threshold binds even when the agent executes the tool itself — but an agent that never routes any traffic through the gateway is caught by the radar, if you have fed the radar, or not at all. That boundary is precisely where a discovery and posture product earns its licence fee.
Coverage runs in opposite directions, which is why the answer is usually both
Noma covers estates. Their published material spans endpoint agents on developer machines discovered through existing EDR or MDM, SaaS agent platforms such as Copilot Studio and AgentForce, and homegrown applications on Bedrock, Azure AI Foundry and Databricks, with an inventory built by connecting through APIs to cloud providers, data platforms, model registries, version control and notebooks. Most shadow AI in a real organisation lives in exactly those places and never presents a credential to anything Token Observe operates.
Token Observe covers one request at a time, deeply. It sees the prompt, the model choice, the tokens, the price, the tool calls that pass through its own MCP endpoint and the tool calls the model merely proposes on the way back. That depth is what makes a different class of rule expressible: block when a card number appears in this prompt, redact this class of data out of this response, park this exact refund on a named approver, refuse because this agent has spent its monthly ceiling, stop because the estimated input size crosses a threshold on this model. None of those are statements about an agent as an object; they are statements about a call being made right now, resolved at step six of eleven, before the payload leaves your network.
Put the coverage statements beside each other and the pairing is obvious rather than clever. Noma answers which agents exist, what they can reach, whether their behaviour has drifted from intent, and how the estate maps to NIST AI RMF or the EU AI Act. Token Observe answers whether this particular payload may go, what it cost, who approved it, and whether the record of that decision can be shown to have survived an operator with database access. There is no shipped Noma connector in Token Observe today and this page does not claim one; what exists is a division of labour that does not overlap enough to make either purchase redundant, plus an inbound feed model — you configure an outbound feed in a console you already administer, and Token Observe holds no credential into your security stack.
Where each one is the right answer
Choose Noma Security when
- The requirement is discovery and posture: knowing which agents, MCP servers, skills and models exist across endpoint, SaaS and homegrown estates, and what each one can reach. Token Observe sees only what presents a credential to its gateway.
- Detection quality is the thing being bought — behavioural analysis across a whole session, red teaming, tunable detectors and maintained policy libraries rather than nine published heuristics with a stated false-negative risk.
- Your procurement gate is a certification. Noma’s homepage displays HIPAA, SOC 2, ISO 27001 and ISO 9001:2015; Token Observe holds none of those and has had no independent penetration test.
- You need compliance reporting mapped to NIST AI RMF, the EU AI Act, ISO 42001, the OWASP LLM Top 10 or MITRE ATLAS as a product feature rather than as a mapping document you assemble yourself.
Choose Token Observe when
- The gap you have found is the action rather than the content: what this agent was allowed to do, who approved this exact payload, what it cost, and whether the ceiling stopped it before egress rather than after the invoice.
- The refusal has to be deterministic and explainable to an auditor — a named permission, a named policy, a named approver — rather than a score somebody has to defend.
- 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 rely on a hosted retention tier.
- Spend is a control rather than a report: hard USD ceilings per request, hour, day and month, reserved before the call leaves your network.
When you would run both
Running both is the normal answer, and it is what Token Observe’s own strategy document instructs rather than a diplomatic conclusion: a generic AI firewall, prompt scanner or red-team platform is a named strategic non-goal, and the stated consequence for this category is to consume threat and identity verdicts from platforms including Noma rather than reproduce them. In that arrangement Noma owns discovery, posture, red teaming and behavioural detection across the whole estate — the endpoint agents, the SaaS copilots and the homegrown applications, most of which never present a credential to a gateway — and its verdict becomes an input to a Token Observe policy rather than a competing decision, because injection scoring is one of seven trigger kinds and a maintained detector belongs in that slot far more than nine patterns do. Token Observe owns the request path for the traffic that does route through it: the payload verdict before egress, action-level deny-by-default permissions, an approval bound to one exact payload and spendable once, hard USD ceilings reserved before the call leaves the network, and a hash-chained record you can key and anchor off the box. The direction of integration is worth noting for the security review: you configure an outbound feed in a console you already administer, so Token Observe holds no credential into your security stack and the worst a compromised deployment can do to it is stop receiving. The honest caveat is that this is a division of labour rather than a shipped integration — there is no Noma connector in Token Observe today, and anyone running both is 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.
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 Noma Security?
Only for one part of what Noma publishes, and the smaller part. Noma’s platform spans AI-SPM discovery and posture, agentic access control, AI red teaming and AI-DR runtime detection across endpoint, SaaS and homegrown agents. Token Observe does none of the discovery, none of the red teaming and only a deliberately modest amount of the detection; its own roadmap names a generic AI firewall, prompt scanner or red-team platform as a strategic non-goal. Where the two genuinely overlap is inline refusal of an agent action, and even there the verdicts derive from different things — Noma’s from behaviour in context, Token Observe’s from action-level permissions, a payload-bound approval and a spend ceiling. If the requirement is knowing what exists and detecting when it misbehaves, that is Noma’s product.
Noma blocks in real time. What does Token Observe add?
Three things, each with a limit. An approval bound to the SHA-256 of one canonicalised action plus its execution context, single-use through a compare-and-set and expiring between one minute and seven days — so a retry with one argument changed is refused as a mismatch; how Noma binds an approval is not described in their published documentation as of 2 September 2026, so ask them. Hard USD ceilings per request, per rolling hour, per UTC day and per UTC month, reserved before egress, with an unpriceable route refused rather than admitted at zero; budgets are likewise not described in their published material, which is a question rather than a finding. And an audit chain whose construction is published in three layers with the weakest named first — unkeyed SHA-256 by default, HMAC-SHA256 under an off-box key, Ed25519 anchoring above that — where every verification result reports which one you actually hold.
Your injection detection is nine regular expressions. Why would anyone take that seriously?
They should not take it as a substitute for Noma’s detection, and Token Observe’s own documentation says so. It is nine weighted patterns over Unicode-sanitised text, scored 1.25 times higher when the text arrived as a tool result because indirect injection arrives through tool output far more often than through the user turn; it has false negatives and the residual risk requires the CISO’s dated written acceptance. Two reasons are given for not doing more: blocking on a classifier with a meaningful false-positive rate would break legitimate traffic on the hot path, and injection findings are one policy input among seven rather than the control. What actually bounds a successful injection is deterministic — the action-level permission the agent did not hold, the approval bound to one payload, the ceiling reserved before egress, the kill switch. Buy the detection from the vendor whose product it is.
Can Token Observe govern a Copilot Studio agent, or Claude Code on a developer’s laptop?
Not on equal terms, and this is where Noma’s published coverage is genuinely wider. Token Observe governs traffic that presents a credential to its gateway — model calls through the OpenAI, Anthropic and Gemini dialects and tool calls through its own MCP endpoint. For managed developer subscriptions there is an endpoint hook that decides locally inside each vendor’s administrator hook against an Ed25519-signed policy bundle, but it is explicitly a preview, reports itself as not production-eligible, and should not be bought as an equivalent to an inline control on an unmanaged device. Noma’s endpoint page describes covering Claude Code, Cursor and Codex through organisation-level agent hooks with agentless discovery via existing EDR or MDM, and their platform names Copilot Studio and AgentForce among the SaaS agent platforms covered. On that estate, ask Noma.
Have you tested Noma Security against Token Observe?
No. Every claim about Noma on this page paraphrases their own published pages read on 2 September 2026 — the homepage, the platform overview, the AI-DR runtime protection page, the AI-SPM page, the AI agent security solution page, the endpoint-agents and homegrown-agents pages, and two posts on agentic access control — and none of it has been independently tested. There has been no witnessed bake-off, and one is named in Token Observe’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 as an instruction to ask Noma rather than as a finding: a capability that is merely undocumented reads identically to one that does not exist, and their product moves quickly. Ask in writing, and ask for the scope and the date.
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Tell us which one you are already running.
If Noma Security 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.
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