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AI Governance & Security
Brad Griffith
Nov 5, 2026
An AI governance platform is software that helps an organization define rules for AI, track the AI it uses, assess risk, and prove compliance. Platforms vary in where they act. Some work on documents and inventories: policies, model cards, risk assessments, and approvals. Others enforce policy while agents run, blocking a call or masking a field in the request path. Workato’s AI Control Plane takes the second approach, governing model, tool, agent, and API traffic through four gateways under one policy, identity, and audit trail.
The test that separates the two is simple: when an agent breaks a rule, does the platform stop it, or only record that a rule existed?
What does an AI governance platform do?
An AI governance platform does four jobs: inventory, policy, risk assessment, and evidence. Inventory lists the models, agents, and tools in use, policy states what is allowed, risk assessment rates each use, and evidence shows auditors what happened.
Most platforms handle the first, second, and fourth through workflows and records. The real difference between platforms is how the policy gets applied to live AI.
What is the difference between design-time and runtime governance?
Design-time governance reviews and approves AI before it goes live, and runtime governance controls what AI does while it is running. You need both. Design-time review catches a risky use case before launch, while runtime enforcement stops a bad call after launch, when the prompt, the data, and the agent’s choices differ from what was reviewed.
Agents make the runtime case stronger. An agent chooses tools at run time, so its behavior cannot be fully reviewed in advance.
How does Workato govern AI at runtime?
Workato governs AI at runtime through gateways, identity, guardrails, approvals, and audit. Gateways authenticate every call and apply the policies an administrator owns. Verified User Access runs agent actions under the end user’s own connection. Genie guardrails screen content and handle sensitive data, approval steps pause an agent for a person, and records capture each step.
Workato Genies offer content-safety screening at low, medium, or high, 31 default PII types plus custom regex types, and actions to tokenize, redact, or refuse. With tokenization the agent never sees the real value, and tokens, not real values, appear in history and log streams.
What does Workato’s AI Control Plane include?
Workato’s AI Control Plane includes the AI Gateway, the AI Registry and Catalog, and AI Security and Governance. The gateway enforces policy on model, MCP, agent, and API traffic. The registry holds approved models, agents, tools, and MCP servers with owners. The governance layer applies one policy and identity model, with consolidated cost and consumption. Workato’s Agent Evals are designed to continually evaluate deployed agents across correctness, tool selection, policy adherence, safety, and outcome quality, so governance continues after deployment.
How does governance map to regulations and frameworks?
Governance programs usually map controls to frameworks such as the EU AI Act, the NIST AI Risk Management Framework, and ISO/IEC 42001. Those frameworks ask for inventory, risk assessment, oversight, logging, and accountability. Enterprise AI agents must operate within regulatory frameworks like GDPR, HIPAA, ISO 42001, and the EU AI Act, with strict access controls, clear audit logs, enforceable data retention policies, and human oversight where regulation requires it.
A platform helps by producing the evidence: who invoked what, under which policy, whether it was allowed, and what it cost. Workato’s audit record links control plane activity to downstream execution detail and can stream to a SIEM.
Workato has achieved ISO/IEC 42001 certification, the international standard for AI management systems. The certification covers Workato’s own AI management practices, including how it evaluates AI risk, governs AI model usage, and maintains accountability across its AI operations. It is independent, third-party validation of how Workato runs its AI practices and does not certify a customer’s own AI programs. Workato’s broader compliance posture spans SOC 1 Type II, SOC 2 Type II, PCI-DSS 4.0.1, ISO 27001:2022, ISO 27701:2019, HIPAA, IRAP, and ISO/IEC 42001. The posture also includes SOC 3 and NIST 800-171A r2.
What should you require from an AI governance platform?
Require five things and test each in a demonstration. Ask to see an inventory that includes AI built outside the platform. Ask to see a policy block a live call. Ask to see an agent acting under a named user’s permissions, and then ask for the audit record of that call. Finally, ask whether a person can be placed in the loop before a consequential action.
Workato shows the identity case with an Okta sign-in, then Salesforce and Slack actions under the user’s own connection, then the logged call with user, client, and duration.
Who owns AI governance in a company?
Several teams share AI governance. Security and compliance set policy and review evidence, IT governance controls access to models, tools, and agents, and finance watches cost. Platform engineering operates the layer, and business owners answer for each agent’s outcomes.
Workato lists the roles: IT governance sets one policy and revokes access in one place, security masks PII and screens for prompt injection, and finance sees spend and charges it back.
How do you start an AI governance program?
Start with an inventory, then enforce at the traffic layer. List every model, agent, and MCP server in use, and route model and tool traffic through gateways so policy applies to every call. Add approvals on consequential actions and review the audit record monthly.
Do not wait for a perfect policy before enforcing. A basic rule applied to every call beats a complete rule applied to some.
An AI governance platform defines, applies, and evidences rules for AI. Platforms differ in whether they only document policy or enforce it at runtime, and because agents choose actions at run time, runtime enforcement matters. Workato governs through gateways, identity, guardrails, approvals, and audit. To test any platform, ask it to block a live call and show the record.
FAQ
What is AI governance?
The set of rules, roles, and controls an organization uses to manage how AI is built and used.
What is the difference between AI governance and AI compliance?
Governance is the program. Compliance is meeting external requirements, which governance helps evidence.
Do I need an AI governance platform?
Once several teams build agents, yes. Inventory, policy, and audit do not scale by spreadsheet.
What is runtime governance?
Controls applied while AI runs: access checks, data masking, approvals, and logging.
Does Workato offer an AI governance platform?
Workato’s AI Control Plane provides governance through gateways, a registry, and security and governance layers.
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