Iron Broad AI governance with real runtime controls.
OneTrust is strong when privacy, data, risk, and compliance teams need one governance layer across models, datasets, agents, vendors, and AI platforms.
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Go beyond OneTrust with a fully managed agent runtime.
OneTrust brings inventory, risk, workflows, monitoring, and runtime policy to AI used across the enterprise. Iron Gorilla adds the managed runtime that builds, runs, contains, and recovers production agents.
Run governed agents, not only the governance layer.
Iron Gorilla owns the agent operating loop. It adds durable work, memory, behavioral trust, human approval, live containment, recovery, managed MCP operations, model fallback, and action-level evidence.
Feature comparison
Compare enterprise governance with managed execution.
Iron Gorilla covers 32 of 44 capabilities. OneTrust AI Governance covers 26. Iron Gorilla includes 18 capabilities OneTrust AI Governance does not.
Managed agent runtimeBuild, run, supervise, and improve agents in one platform.10 features
Fully managed agent runtimeBuild, deploy, execute, and operate production agents in one accountable service.
Plain-language Agent BuilderCreate and configure production agents without assembling the runtime.
Durable execution across service interruptionResume long-running work after workers, networks, or providers fail.
Managed long-term agent memoryKeep governed context across sessions and long-running goals.
Long-running goal managementTrack plans and progress for work that spans many steps or sessions.
Managed multi-agent orchestrationCoordinate specialized agents, dependencies, handoffs, and shared work.
Runtime telemetry and operational monitoringSee agent activity, policy events, quality, drift, safety, and performance.
Configurable approval workflowsRoute higher-risk decisions to named people with recorded sign-off.
Checkpoint resume and operator replayRestart governed work from a known good point after intervention.
Action compensation and rollbackReverse or compensate for a completed business action when recovery requires it.
Policy and evidenceControl actions before they happen and keep proof of each decision.15 features
Enterprise AI portfolio registryMaintain governed records for models, datasets, agents, vendors, owners, and lifecycle.
Shadow AI discoveryFind undeclared AI services and embedded AI use across the enterprise.
AI component dependency mappingShow how models, datasets, agents, vendors, and systems depend on one another.
Regulatory templates and control mappingsStart governance with current frameworks, assessments, and mapped controls.
Automated AI risk tieringClassify AI systems by use, impact, data, and regulatory exposure.
Portfolio governance workflowsCoordinate intake, assessment, ownership, attestation, approval, and remediation.
Automated evidence and audit outputsGenerate review-ready records from governance and runtime activity.
Continuous risk, quality, safety, and drift monitoringWatch AI behavior and operational signals after deployment.
Prompt, output, and sensitive-data controlsInspect and filter prompts and outputs before sensitive data or unsafe content passes.
Block or allow agent actions by policyEnforce permitted behavior before tools change business systems.
MCP policy enforcement and audit logsControl MCP permissions and retain records of tool activity.
Behavioral Trust ProfilesScore each agent from behavioral signals and make trust visible to operators.
Adaptive autonomy from earned trustTighten or relax oversight as observed behavior changes.
Live containment and governed recoveryIsolate risky agents, connections, or work and guide operators through recovery.
Immutable or tamper-evident audit trailPreserve trustworthy records of policy, approvals, actions, models, and outcomes.
Connections and modelsControl MCP tools, model traffic, routing, cost, and integrations.9 features
Prebuilt enterprise integrationsConnect governance and agent operations to existing enterprise systems.
APIs, SDKs, and data feedsExtend platform functions into engineering and governance workflows.
Governance across external AI platformsApply one governance plane to AI workloads that run in other products and clouds.
MCP connection ownership, health, and lifecycleOperate MCP connections with owners, scopes, credentials, health, risk, and history.
Policy-aware multi-model routingSelect approved models by capability, cost, latency, risk, and policy.
Automatic model-provider fallbackContinue governed work on an approved backup when a provider fails.
Spend attribution, budgets, and cost routingTrack model cost by agent and enforce limits before spend runs away.
Enforced per-agent tool permissionsGive each agent only approved tools and actions.
Model and vendor lifecycle recordsKeep internal models and third-party AI providers inside the governance program.
Enterprise and governmentProtect identity and data across private and public-sector work.10 features
Managed SaaS deliveryUse the platform as an enterprise cloud service.
Customer-controlled on-premises deploymentOperate the platform inside infrastructure controlled by the buyer.
Air-gapped deployment patternOperate inside disconnected environments with controlled model and data access.
SOC 2 Type II assuranceProvide independent assurance that service controls operated effectively over time.
Broad certification portfolioSupport enterprise assurance through multiple independent certifications and attestations.
TX-RAMP authorizationSupport procurement for eligible U.S. state and local government cloud use.
Published package structureShow buyers a public package and pricing basis before the sales process.
Formal partner ecosystemUse technology, services, consulting, and implementation partners.
Dedicated defense and public-sector agent solutionAddress mission operations, deployment, approval, and evidence needs directly.
Third-party AI vendor governanceRegister external AI suppliers and maintain their risks and lifecycle records.
The short answer
Enterprise governance layer or complete agent operations?
Use OneTrust when privacy, data, risk, and compliance teams need discovery, inventory, regulatory workflows, runtime policy, and evidence across AI systems run on many platforms.
Choose Iron Gorilla when teams need one managed platform for agent creation, durable execution, approvals, behavioral trust, containment, recovery, MCP operations, model controls, and evidence.
Switch with less risk
Already under contract with OneTrust?
Your organization may qualify for a contract buyout and complimentary professional services when it moves agent operations to Iron Gorilla.
Check eligibilityFAQ
OneTrust AI Governance alternative questions
Is Iron Gorilla an alternative to OneTrust AI Governance?
Yes. Both govern enterprise AI. OneTrust is broader across the AI portfolio, while Iron Gorilla adds the managed runtime for durable and controlled agent operations.
What is the main difference between OneTrust and Iron Gorilla?
OneTrust governs AI across external platforms. Iron Gorilla builds, runs, governs, contains, and recovers the agent workload in one managed runtime.
Does OneTrust provide runtime controls for AI agents?
Yes. OneTrust publishes prompt and output filtering, sensitive-data controls, action allow or block policy, MCP permissions, monitoring, and audit logs.
Does OneTrust run production agents?
OneTrust governs and monitors agents across other platforms. It does not publish a fully managed runtime that builds, executes, resumes, and recovers customer agents.
When should a team choose Iron Gorilla over OneTrust?
Choose Iron Gorilla for durable execution, managed memory, behavioral trust, runtime approvals, containment, recovery, MCP health, model fallback, and cost controls.
Can Iron Gorilla buy out a OneTrust contract?
Your organization may be eligible for a contract buyout and complimentary professional services. Eligibility and scope depend on your current contract and migration needs.
See the full agent lifecycle.
Build an agent. Set its limits. Run it with trust, approval, and audit controls.
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