How VeriProof compares, and when to pick something else
Most tools in this space answer a security or inventory question. VeriProof answers the business one: is this action allowed under our rules, who decides when it isn't, and can everyone who signs off see the same record?
The short version
Scroll horizontally to see all columns.
| Option | What it leads with | Usually bought by | How VeriProof fits |
|---|---|---|---|
| LangGuard | Allow, block, or escalate at the moment an agent acts | AI builders, internal controls, security | The closest overlap. Compare them directly on your workflow. |
| Airia | Finding, securing, and governing AI, with policy checks before a tool call runs | Security and IT | Start there to find agents. Compare who decides the exception and who approves a rule change. |
| Holistic AI | A governance platform plus Guardian Agents that block, redirect, or escalate | AI governance, risk, and compliance | Real runtime overlap. Compare who the escalation reaches and what a reviewer can check. |
| OneTrust AI Governance | AI inventory and risk programs, now with runtime actions by policy | Privacy, risk, and compliance | Real runtime overlap. Compare who gets the exception and what the auditor can verify. |
| ServiceNow AI Control Tower | One place to discover, govern, secure, and measure AI across the enterprise | IT and risk teams that run on ServiceNow | Start there to find agents. VeriProof decides each business action for the agents you connect. |
| IBM watsonx.governance | Lifecycle governance, risk, and monitoring for models and agents | Model risk, data science, and compliance | Model risk is its home ground. VeriProof works on individual agent actions. |
| ModelOp | An AI system of record that automates the lifecycle from intake to retirement | Model risk and AI governance offices | Keep it for inventory and approvals to deploy. VeriProof governs what agents do once live. |
| Credo AI | Running the AI governance program: registry, risk, policy | GRC and AI governance teams | Credo sets the program; VeriProof runs the decision at the point of action. |
| Zenity | Securing agents across SaaS, cloud, and endpoints | Security leaders | Security handles threats; VeriProof handles business rules and approvals. |
| Noma Security | Discovery, posture, red teaming, and runtime protection | Security teams | Works alongside it on the business-rule decision and its record. |
| Lakera (Check Point) | Runtime defense against prompt attacks, data leakage, and unsafe agent behavior | Security teams | Keep it for threats. VeriProof adds the routing and the record. |
| Guardrails AI | Open-source validators that check what goes into and comes out of a model | AI engineers | Keep the guards. VeriProof adds the routing and the record. |
| Microsoft Agent 365 | Inventory, identity, and security for agents | IT and security | A complement. See the Agent 365 page. |
| Build it yourself | Approval steps in your agent framework plus logs | Engineering | Covers the first agent; the handoffs and records are what get expensive. |
Checking an action against a rule is the part most of these tools share. The differences show up after the check. Who decides the gray cases? Who changes the rule? Who can prove what happened?
Descriptions of other products come from each company's own website, September 2026.
Where VeriProof stands apart
Several vendors here now block actions or send them to a person, and we credit them for it below. So the useful questions are about what surrounds the check. Here's where VeriProof differs today:
- Rule changes follow the approvals you set. You choose how many approvers besides its author a change needs, up to two before it's enforced. A governance engineer's or developer's change to a production agent always goes to an administrator or compliance officer. Shadow can show what a change would have decided before it counts.
- Missing data becomes a fix request. When a rule needs a field the agent doesn't send, the developer gets a request naming it.
- A portal for each of six roles. Business owner, compliance officer, governance engineer, developer, auditor, and administrator, with the work handed off between them. Exceptions go to whoever holds the authority: a named person, a role, or a group that claims it. Risk acceptance is decided by the named business owner.
- Compliance built from the agents' own decisions. Framework coverage comes from recorded decisions, control by control, and every gap gets an owner and a due date.
- One sealed record for the fleet. Every agent you connect writes to the same record, backed by Azure SQL Database Ledger by default.
- Independently verifiable. A reviewer outside your team can verify the record quickly, on their own. See how.
- Prices on the page. Pricing is published, so you can size it before a sales call.
LangGuard: the closest overlap
LangGuard's Arbiter checks each action after the agent decides and before the tool call runs. It allows the action, blocks it, or escalates it to a named approver. It also keeps a tamper-evident action ledger and looks for segregation-of-duties conflicts across what each agent can reach. Those are real strengths.
With that much shared ground, the differences sit around the decision rather than in it, and they're the ones listed above. The honest advice is to run both on the same workflow and see which one your reviewers would rather work in.
Airia: discovery and tool-call control in one platform
Airia starts by finding AI. It inventories every AI tool, model, agent, and MCP server in use, including the ones nobody approved. VeriProof doesn't do discovery, so if you don't yet know which agents you have, Airia is a strong place to start.
Its Agent Constraints engine checks each call "before the tool call fires." A policy can allow the call, block it, narrow its parameters, or start an approval. New policies can run in a monitor mode first. Since August, inline Security Runbooks can resume, block, or escalate a flagged call for human review. Airia also builds agents and offers red teaming and an MCP gateway.
On one shared workflow, check who approves a policy change and whether it can be the person who wrote it. Then see who an exception reaches, what happens when it sits, and what an outside reviewer can check afterward.
Holistic AI: a governance program with agents that enforce it
Holistic AI covers the program end to end. It discovers models, agents, and applications and tracks their owners and risk levels. It tests for bias, hallucination, privacy, and robustness, and maps controls to the EU AI Act, NIST AI RMF, and ISO/IEC 42001.
Guardian Agents carry that into runtime. When risk crosses a threshold, they can block a request, redirect a workflow away from a failing component, or escalate with full context to a human reviewer.
VeriProof doesn't test models for bias or keep a model inventory. If you want one vendor for the whole program, Holistic AI deserves a close look. Our focus is narrower: each exception goes to the person, role, or group that holds the authority for it, with a deadline, reminders, and an alert to your administrators if it sits. Give both the same three cases and compare what each reviewer sees.
OneTrust and Credo AI: the governance program, reaching into runtime
Both help you run an AI governance program: an inventory of AI systems, risk assessments, and policies mapped to frameworks like the EU AI Act and NIST AI RMF.
OneTrust now reaches into runtime too. Its AI Governance page promises to "block, allow, redact, escalate, or route actions by policy." Its AI Guard SDK finds sensitive data in prompts and responses and blocks or redacts it. If OneTrust already holds your program, test it next to VeriProof on one workflow. Watch who the escalation reaches, how a rule change gets approved, and whether your auditor can check the record without logging in.
Credo AI has previewed Agent Governor, which applies policy to agent actions, starting with coding agents. Today, Credo sets the program and VeriProof runs the decision for each action. If the program says refunds above a limit need a supervisor, VeriProof makes that happen for each refund and keeps the record.
ServiceNow AI Control Tower: when ServiceNow is where the work already lives
ServiceNow describes AI Control Tower as one place to discover, observe, govern, secure, and measure AI, including agents built elsewhere. It inventories agents, models, and MCP servers, and ships governance content for the EU AI Act and NIST AI RMF. ServiceNow says it can shut down an agent that goes beyond its permissions. Its AI Gateway, generally available since September, adds approval status for MCP tools and runtime pause controls.
If your IT and risk teams live in ServiceNow, that's a strong reason to start there. It's also the better tool for finding agents you didn't know you had. What we add is the decision on each business action. VeriProof checks a specific refund, payment, or claim against rules your risk team wrote, and sends the exception to the person who holds that authority.
IBM watsonx.governance and ModelOp: model risk, now stretching to agents
These two come from model governance: an inventory of models and use cases, risk ratings, sign-off before deployment, and monitoring for drift, bias, and performance.
IBM watsonx.governance offers what IBM calls a living map of your AI, including unapproved use. It maps to the EU AI Act, NIST AI RMF, and ISO 42001. For agents, IBM adds evaluation metrics and production monitoring with alerts. Its Enforcement Tracking records whether watsonx Orchestrate agents stay within policy thresholds. It runs in the cloud or on premises, and IBM publishes indicative starting prices.
ModelOp calls itself an AI system of record. It automates the lifecycle from intake to retirement across ML, generative AI, agents, and vendor AI. It can import agents from MCP and A2A servers, block unapproved agents through a proxy, and guard against prompt injection and PII leakage.
If your main need is model inventory, validation, and sign-off before deployment, pick one of these. VeriProof works one level down, on what a connected agent does: whether this payment follows the rule, who approves the exception, and what the auditor sees. The two fit together.
Zenity and Noma Security: security first
Both started from the security team's problem. Zenity secures AI agents across SaaS, cloud, and endpoints, with runtime boundaries that can allow an action, block it, or quarantine the agent. Noma covers discovery, posture management, red teaming, and runtime protection, and a triggered policy can block an action or route it to a human.
If your first worry is prompt injection, shadow agents, or data leaking through a tool, start there. VeriProof isn't a threat detector. It asks whether this refund, payment, or claim follows the business's rules, and whether the right person approved the exception. Plenty of teams will want both.
Lakera and Guardrails AI: keep them, add the next step
Lakera, now part of Check Point, screens prompts, tool use, and responses for prompt attacks, data leakage, harmful content, and malicious links. Check Point says it works in more than 100 languages, and its Agent Security product discovers agents and rates their risk.
Guardrails AI is an open-source framework, under the Apache 2.0 license, for input and output guards. Validators from its Hub check for PII, toxic language, prompt injection, and more. When one fails, the guard can fix the output, re-ask the model, refuse, or raise an exception.
VeriProof doesn't try to replace either. Lakera's docs leave the next step for a flagged request to your application, and that's the moment to call VeriProof. Send the guardrail result with the policy check, and your rules pick the next step. See how it works with guardrails.
Microsoft Agent 365
Agent 365 manages which agents you have, the identity each one uses, and the security and data controls around them. VeriProof decides whether a specific business action is allowed and who approves the exceptions. Read how they work together.
Building it yourself
Most agent frameworks can pause for a human approval, and every team can write logs. For one agent, that's often enough. By the tenth, you need:
- Rules that risk and compliance write, version, and try in Shadow before they take effect.
- Approvals that reach whoever holds the authority, a person, role, or group, with a deadline and reminders.
- Fix requests to the developer when a rule needs data the agent doesn't send.
- Separate views for the business owner, compliance, and auditors, each with the right access.
- Records a reviewer outside your team can check, with retention and erasure that hold up.
Each item is a project. Together they're a product, and that's the one VeriProof already built. Watch forty agents join both ways, and see what changes for the people who support them.
- Agents
- 40
- Separate setups to run
- 5
- Setups to keep in step
- 10
- Handoffs routed by hand
- 200
- Yearly cost (est.)
- $2.18M
Each kind of agent covers part of this table with its own tools, and none covers all of it. People fill the gaps by hand, and each new kind adds another setup to keep in step with the rest.
- Agents
- 40
- Separate setups to run
- 1
- Setups to keep in step
- 0
- Handoffs routed by hand
- 0
- Yearly cost (est.)
- $855K
With VeriProof, every agent you connect uses the same reviewers and record. Each agent keeps its own rules, and you manage them all in one place.
Estimates from the business value calculator.
When VeriProof isn't the answer
Some needs sit close to ours and belong to someone else:
- Finding agents you don't know about. For discovery, look at Airia, ServiceNow AI Control Tower, Microsoft Agent 365, Noma, or Lakera's Agent Security, and connect what they find.
- Model inventory and model risk. ModelOp, watsonx.governance, and Holistic AI are built for it.
- Prompt injection and content safety. Lakera, Guardrails AI, Zenity, and Noma cover it. VeriProof runs beside them.
Test it the fair way
Pick one workflow and give every vendor the same three cases: a routine action, an exception that needs a person, and an action that must not happen. Then watch who the exception reaches and what your auditor gets afterward. Use our shortlist method, or start with the agent you already have.