Best 10 Agentic AI Governance Solutions (2026)

• Complete Agentic Identity lifecycle management from onboarding to offboarding for all human and non-human identities
• All AI agents and MCPs automatically discovered and placed into a unified directory
• AI gateway provides a central point for managing all identities and workflows
• AI device trust provides real-time verification of hardware health to ensure agents cannot run on compromised devices
• Human-in-the-loop governance allows admins to define risk-based checkpoints, to approve or deny agent actions
• Full IAM/MDM platform including SSO, MFA, password management, and patching, built on Zero Trust principles
• Conditional access policies based upon context-aware access decisions are applied to both human and non-human identities
• More features coming soon including audit reporting, conditional access and agent to agent trust

JumpCloud automates many of the challenges associated with governing AI identities. It builds out a complete map of all of the AI agents running in your environment. It tells you where they are coming from, what types of connections they use and when they were created. It integrates with any AI system and external identity provider and provides real-time risk monitoring and device health checks with continuous governance over all agents in your organization. We recommend JumpCloud as a strong option for mid-sized SMBs up to enterprise sized teams looking for an all-in-one agentic IAM platform.

• Discovers and catalogs every AI agent into an inventory with real-time visibility
• Continuous behavior analysis for AI agents, with policy-based controls
• Visibility into prompts, tool calls, decisions, and agentic behaviors
• Custom evaluations with numeric reliability signals
• Compare prompt outcomes and agentic logic to improve reliability
• Real-time detection and alerts if AI performance degrades
• Secures sensitive data against AI access

Arthur is best suited to teams building agentic systems in-house who need to deploy and monitor them with full visibility. It stands out for its built-in guardrails. Risky agent behaviors are stopped before they cause damage in production, rather than after. The platform works with any AI provider and offers flexible deployment across cloud, VPC, or on-prem environments. The free tier and transparent pricing make it easy to evaluate. We’d recommend Arthur to engineering-led teams governing custom agentic workflows.

• All aspects of Agentic governance are managed from a single view
• Provides a complete view into AI agents to identify shadow AI
• Allows you to develop customized agent risk definitions and apply specific rules
• Identifies sensitive permissions and stale owners, allowing you to prioritize these for governance
• Assigns ownership automatically, based on information from real-time activity
• Retires unused AI agents to reduce attack surface and AI spend

AvePoint expands its data governance offering with AgentPulse. It’s a great fit for organizations already working with AvePoint across M365 or Google Workspace environments. The platform automatically assigns ownership based on real activity, retiring unused agents at the appropriate time. This has the dual benefit of cutting AI spend, whilst reducing attack surface. AvePoint also comes with guided workflows to help non-security teams make the right choice in terms of security and compliance. The platform works best within Microsoft estates. We’d also recommend it for use by teams who have already deployed AvePoint across other areas.

• Catalogs every AI system, agent, vendor, and model, providing full visibility into shadow AI
• Comprehensive risk classification and dependency mapping
• Continuously calculates risk, identifying tool misuse, scope drift, and inter-agent risk
• Preset policy library aligned to regulatory frameworks, designed for the auditing process
• Compatible with EU AI Act, NIST AI RMF, ISO-42001, OMB M-25, CO ADMT, and NAIC AI
• Integrations with Snowflake, Databricks, AWS, Azure, ServiceNow, Jira, Confluence, Slack, GitHub, and MLflow

Credo AI is built for governance and compliance teams, rather than engineers. The platform’s policy library spans the EU AI Act, NIST AI RMF, ISO 42001, and other frameworks. This makes it a strong choice for regulated enterprises and industries, requiring audit-ready evidence without having to build the policies themselves. GAIA reduces the amount of manual work involved in tracking risk, providing continuous risk scoring to flag risk and drift. We found the trade-off is complexity. Some users have noted a steep learning curve, with a setup process that requires time and resourcing to enact properly. We would recommend Credo AI to GRC-focused enterprises with the skills and expertise to run it properly.

• Continuous monitoring and auditable evidence
• Designed to operate and scale in SaaS, VPC, and AWS GovCloud environments
• Real-time guardrails that detect and prevent jailbreaks and exposure
• Analyze the root cause of issues for fast resolution and continued improvements
• Models only process approved inputs, with developers only receiving approved outputs

Fiddler AI is designed for teams that own their agentic systems and need observability into the pipeline, rather than adding it as an extra. The platform stands out thanks to transparent, usage based pricing and free guardrails. These guardrails identify jailbreaks and prompt injection before they reach production, with root cause analysis helping teams to fix the underlying issues. Deployment across SaaS, VPC, and AWS GovCloud makes it great for customers in regulated areas. Some users find there is a steep learning curve, despite the streamlined and clean interface. We would, therefore, recommend Fiddler AI to engineering-led teams that want to create strong governance processes.

• Automatically detect models, agents, APIs, pipelines and workflows across AWS, Azure, GitHub, Databricks and others
• Identify shadow AI, then classify it based on risk level, owner, lifecycle stage, and business purpose
• Maintain and manage a live AI inventory with continuous real-time monitoring
• Delivers AI red teaming with dynamic adversarial testing
• Jailbreak resistant and prompt injection detection
• Designed for compatibility with frameworks like EU AI Act, NIST AI RMF, ISO 42001, and NYC Local Law 144

Holistic AI begins with compliance. This suits regulated enterprises, requiring the ability to demonstrate compliance with EU AI Act, NIST AI RMF, ISO 42001, and NYC Local Law 144. The platform detects and classifies shadow AI automatically, prioritizing it based on risk level, owner, and business purpose. It then adds AI red teaming and jailbreak resistance testing to assure security. In 2026, Holistic AI introduced its Guardian Agents which extend the platform from passive monitoring to real-time intervention. We think Holistic AI works well alongside a dedicated MLOps tool, rather than replacing it entirely.

• Coordinates agents, tools, workflows, and foundation models across the enterprise
• Intelligent routing sends workloads to the right agent or tool in real time, allowing you to reduce fragmented handoffs
• Unifies agent ecosystem using prebuilt, partner, and custom agents within a single system
• The platform coordinates and supervises agents, dividing workloads
• Create agent decision patterns to manage how agents behave at different stages
• Throughout their activities, IBM allows you to maintain visibility of agents to monitor performance

IBM WatsonX Governance is built for large organizations, operating in regulated environments, rather than lean security teams. It runs with a library of over 200 frameworks and policies, ensuring that it can map obligations directly onto AI systems, producing auditable reports. The platform will also coordinate AI agents, tools, and foundation models, using smart routing to ensure that handoffs are clean. With any platform this extensive, there is going to be some degree of learning curve. If you have the resources and the team to dedicate to the platform, then it’s a great option. The pricing of the platform scales with evaluation volume, and can rise rapidly for smaller teams.

• Standardizes AI use case intake and registration, creating a full lifecycle record for every use case
• Auto-generates risk tiers and initial controls for each use case
• Runs continuous automated testing for bias, drift, and performance
• Enforces approval workflows and policy checks prior to deployment
• Integrates with over 50 enterprise AI, MLOps, and IT systems

ModelOp was designed for the banking and financial sector. It provides centralized, auditable inventory alongside standardized intake and approval workflows. The platform auto-generates documentation including audit reports, ensuring regulatory compliance can be proved. The platform manages traditional ML, generative AI, and agentic AI under a single platform, rather than separating them into separate streams. Some users flag a steep learning curve and that implementation timelines can be long. We’d recommend ModelOp for larger, regulated organizations with internal expertise to run an in-depth governance platform.

• Assign ownership and lifecycle status, with telemetry across AI platforms
• Gain insights into component dependencies and relationships, then track these to monitor drift, quality, safety and performance
• Track models, datasets, agents, and vendors via a centralized inventory
• Continuous model and agent observation
• Enrich runtime signals with regulatory and usage context, allowing you to prioritize risk using data sensitivity
• Allows you to detect and log AI policy violations in real time, identify PII and sensitive data, helping you to surface risks before incidents occur

OneTrust extends its privacy and GRC platform into the AI governance space, making it a natural fit for organizations already utilizing OneTrust for compliance purposes. The platform’s governance templates are pre-mapped across EU AI Act, NIST AI RMF, and ISO 42001, ensuring that teams can hit the ground running. Continuous monitoring and real-time policy violation detection help flag PII exposure before it leads to compromise. The platform was built around privacy and GRC, with AI governance added as a newer module. We’d recommend OneTrust to privacy and GRC-led teams, rather than SecOps teams looking for agent specific runtime controls.

• AI Security Posture Management: allows you to enforce secure-by-design policies to manage agent permissions, access, and memory, thereby limiting exposure
• AIDR: detection and response capabilities allow you to monitor and correlate behavior with context, ensuring that unsafe actions can be prevented from happening
• Comprehensive visibility: ensures that you can account for all agents, with information on what they can access, who owns them, and what behavior is permitted within the environment
• Guardrails: these ensure that you can avoid risky configurations and over-permissioned agents from reaching runtime
• Contextual response: as the platform has comprehensive visibility, you can understand execution paths, enabling faster identification and mitigation of sophisticated threats

Zenity is designed for SecOps teams, rather than compliance ones. This distinction shows in its focus on runtime detection and response, instead of policy alignment. Its AISPM and AIDR capabilities ensure secure by design permissions, correlating agent behavior with context to flag risky configurations. Zenity’s narrow focus is its strength, and its weakness. Customers report drastic reductions in security violations and high rates of automatic remediation. However, Zenity’s focus on the agent layer means that it doesn’t extend into broader identity, budget, or audit trail governance. We’d recommend Zenity to security teams in large, Microsoft-focused organizations, aware of the platform’s capabilities and drawbacks.