Best 5 tools for AI governance in 2026

TL;DR

AI governance has evolved from aspirational principles to operational necessity as organizations deploy AI systems at scale. The top 5 AI governance tools for 2026 address different dimensions of responsible AI: Bifrost by Maxim AI leads with infrastructure-level governance through budget controls, access management, and audit logging, integrated with Maxim's comprehensive quality evaluation and observability platform. Credo AI provides lifecycle governance with automated compliance. IBM Watsonx.governance offers enterprise-grade risk management integrated with IBM's AI stack. OneTrust AI Governance specializes in GRC workflows for heavily regulated industries. Microsoft Azure AI delivers cloud-native governance for Azure-deployed models. Organizations should evaluate tools based on governance scope (infrastructure vs. compliance), performance impact, integration needs, and operational requirements.

Table of Contents

1. Introduction
2. Understanding AI Governance in 2026
3. Why AI Governance Matters More Than Eve
4. The Best 5 AI Governance Tools
   • Bifrost by Maxim AI
   • Credo AI
   • IBM Watsonx.governance
   • OneTrust AI Governance
   • Microsoft Azure AI
5. How to Choose the Right AI Governance Tool
6. Building a Comprehensive Governance Strategy
7. Conclusion

Introduction

The AI governance landscape has transformed dramatically. What started as abstract ethical principles has evolved into concrete operational requirements driven by regulatory mandates, stakeholder expectations, and production realities. By 2026, Gartner predicts that 75% of large enterprises will adopt dedicated AI governance platforms, driven by frameworks like the EU AI Act, NIST AI Risk Management Framework, and industry-specific regulations.

The AI governance market reflects this urgency. From $400M in 2023 to a projected $5.8B by 2029, the explosive growth at a 45.3% CAGR signals that organizations are moving beyond ad hoc oversight to systematic governance infrastructure. Yet the proliferation of tools creates decision paralysis. Traditional compliance vendors add AI features. Cloud providers extend their platforms. Specialized AI governance startups emerge with focused solutions.

This guide evaluates the top 5 AI governance tools based on their approach to critical governance dimensions: infrastructure controls, quality assurance, risk management, compliance automation, and operational transparency. We examine how each platform addresses the governance challenge, with particular focus on Bifrost by Maxim AI, which uniquely provides infrastructure-level governance at the AI gateway layer, combined with comprehensive quality assurance through the Maxim platform.

Understanding AI Governance in 2026

AI governance refers to the frameworks, policies, and tools that ensure artificial intelligence systems are developed, deployed, and operated responsibly. It encompasses multiple dimensions that work together to build trustworthy AI:

Infrastructure Governance

Infrastructure-level governance enforces controls at the layer where all AI requests flow. This includes access control, authentication, budget enforcement, rate limiting, audit logging, and secrets management. Unlike application-level controls that can be bypassed, infrastructure governance operates at the gateway layer where it cannot be circumvented. Bifrost exemplifies this approach by providing comprehensive governance controls with 11µs overhead, meaning governance doesn't impact performance.

Quality and Reliability Governance

AI quality has become a governance imperative. Organizations must ensure their AI systems produce accurate, relevant, and safe outputs consistently. This requires systematic testing, evaluation, and monitoring throughout the AI lifecycle. Quality governance addresses questions like: Does my customer support agent provide correct information? Do my code generation tools produce secure code? Is my AI making decisions that align with business objectives?

Compliance and Regulatory Governance

Regulatory frameworks like the EU AI Act, which entered force in 2024 with full enforcement by 2026, create binding obligations for AI systems. Organizations must demonstrate compliance through documentation, risk assessments, impact evaluations, and audit trails. Compliance governance translates regulatory requirements into operational controls.

Risk and Safety Governance

AI reliability requires identifying and mitigating risks before they impact users. This includes technical risks (model failures, hallucinations, security vulnerabilities), ethical risks (bias, fairness issues), and operational risks (system degradation, unexpected behavior). Risk governance implements controls that prevent, detect, and respond to AI failures.

Transparency and Explainability Governance

Stakeholders increasingly demand visibility into how AI systems make decisions. Transparency governance ensures appropriate documentation, explainability mechanisms, and clear accountability for AI outcomes. This becomes particularly critical for high-stakes applications in healthcare, finance, and government.

Operational Governance

As AI moves from experimentation to production at scale, organizations need governance that integrates with development workflows. Operational governance ensures teams can iterate quickly while maintaining oversight, enables cross-functional collaboration, and provides the visibility needed for continuous improvement.

Modern AI governance tools address these dimensions with varying emphases. Some focus on compliance automation and audit readiness. Others prioritize quality assurance and production monitoring. The most effective governance strategies combine tools that address complementary governance needs.

Why AI Governance Matters More Than Ever

The stakes for AI governance have escalated dramatically across three critical dimensions:

Regulatory Pressure

The EU AI Act represents the world's first comprehensive AI legislation. It classifies AI systems by risk level and imposes obligations ranging from transparency requirements to prohibited uses. Organizations deploying high-risk AI systems face mandatory conformity assessments, documentation requirements, and potential fines up to €35M or 7% of global revenue.

The U.S. has taken a more fragmented approach. The NIST AI Risk Management Framework provides voluntary guidance. Executive orders establish requirements for federal AI use. State-level regulations like California's AI safety bills create additional compliance complexity. Organizations operating globally must navigate this patchwork of requirements.

Operational Complexity

AI deployments have moved from experimental pilots to production systems handling millions of interactions. A conversational banking assistant processes thousands of customer queries daily. A code generation tool influences developer productivity across an organization. A content moderation system makes decisions affecting user experience at scale.

This operational complexity makes manual oversight impossible. Teams need systematic governance that scales with deployment, identifies issues before they impact users, and enables rapid iteration without compromising safety.

Stakeholder Expectations

Customers, employees, and shareholders increasingly scrutinize AI practices. According to Gartner, 79% of corporate strategists now consider AI and analytics critical to business success. But trust is fragile. A single high-profile AI failure can damage reputation, trigger regulatory investigation, and undermine stakeholder confidence.

Organizations that demonstrate responsible AI practices through robust governance gain competitive advantage. Those that treat governance as an afterthought face mounting risks.

The Best 5 AI Governance Tools

1. Bifrost by Maxim AI

Bifrost represents a fundamentally different approach to AI governance. While traditional governance platforms focus on compliance documentation and post-deployment auditing, Bifrost provides infrastructure-level governance at the AI gateway layer, combined with Maxim's comprehensive quality platform for end-to-end oversight.

This architectural approach means governance is enforced at the infrastructure level where every AI request flows, rather than relying on application-level controls or manual processes. Bifrost governs access, budgets, and security while Maxim's platform ensures quality, safety, and reliability.

Platform Overview

Bifrost is a high-performance AI gateway built in Go that delivers governance through the infrastructure layer. Every LLM request in your organization flows through Bifrost, making it the natural enforcement point for governance controls. The zero-configuration deployment means teams can implement infrastructure governance in under 60 seconds without complex setup.

The integration with Maxim's AI quality platform creates a comprehensive governance solution: Bifrost handles infrastructure controls (access, budgets, security), while Maxim ensures quality through evaluation, simulation, and production observability.

Infrastructure Governance Capabilities

Enterprise Access Controls and Authentication

SSO integration with Google and GitHub provides centralized authentication for all AI access. Virtual keys prevent API key exposure while enabling fine-grained access control. Teams can manage who accesses which models without exposing actual provider credentials.

This infrastructure-level authentication means governance policies are enforced automatically. Developers cannot bypass access controls. Every request is authenticated, authorized, and logged.

Hierarchical Budget Management

Budget governance operates at organization, team, and customer levels. Set spending limits that enforce automatically without manual intervention. Track consumption in real-time. Alert when budgets approach thresholds. Hard limits prevent overruns.

This financial governance prevents the runaway costs that plague AI deployments. Unlike compliance platforms that simply report spending, Bifrost enforces limits at the infrastructure layer where requests are actually made.

Comprehensive Audit Logging

Every request flowing through Bifrost generates detailed audit logs capturing user identity, model used, tokens consumed, costs incurred, and response metadata. These audit trails satisfy compliance requirements while enabling security investigations.

Export logs for long-term retention and analysis. Integration with SIEM platforms enables security monitoring. The audit data provides the foundation for compliance reporting and regulatory submissions.

Secrets Management and Security

HashiCorp Vault integration provides enterprise-grade secrets management. API keys are stored securely, rotated automatically, and never exposed to applications. This infrastructure-level security control prevents credential leakage.

Rate limiting protects against abuse and controls costs. Virtual keys enable secure delegation without exposing actual credentials. These security controls operate transparently at the gateway layer.

Performance Without Governance Overhead

Traditional governance platforms add latency and complexity. Bifrost's 11µs overhead at 5,000 RPS means governance enforcement doesn't impact performance. The Go-based architecture handles high-throughput workloads while maintaining sub-millisecond latency.

This performance characteristic is critical for governance. If governance makes AI applications slow or unreliable, teams will bypass it. Bifrost's performance ensures governance never becomes the bottleneck.

Quality Governance Through Maxim Platform Integration

Infrastructure governance alone is insufficient. Organizations need quality assurance, risk mitigation, and production monitoring. Bifrost's integration with Maxim's platform addresses these dimensions:

Pre-Deployment Quality Validation

Before AI applications reach production, Maxim's evaluation framework validates quality through systematic testing:

• Simulation-Based Testing: Validate AI agents across hundreds of scenarios to identify failure modes before deployment
• Multi-Level Evaluation: Assess quality at session, trace, and span levels using deterministic, statistical, and LLM-as-judge evaluators
• Human-in-the-Loop Review: Collect expert judgments on nuanced quality dimensions that automated metrics cannot capture

This pre-deployment governance reduces the risk of shipping AI systems with safety, accuracy, or compliance issues. Teams can quantify quality improvements and make data-driven deployment decisions.

Production Quality Monitoring

LLM observability extends governance into production environments:

• Real-Time Quality Checks: Automated evaluations run on production traffic to detect quality degradation
• Agent Tracing: Track request flow through multi-agent systems to identify bottlenecks and failures
• Custom Dashboards: Visualize quality metrics across dimensions to optimize agent behavior
• Alerting: Get notified immediately when quality metrics fall below thresholds

This production governance ensures quality doesn't degrade post-deployment and provides rapid issue resolution.

Cross-Functional Governance Workflows

AI governance requires collaboration between engineering, product, operations, and compliance teams. The Bifrost-Maxim combination facilitates this:

• No-Code Evaluation: Product teams define quality requirements without writing code
• Shared Visibility: Cross-functional dashboards for quality metrics, costs, and system behavior
• Version Control: Track changes to prompts, workflows, and governance policies
• Audit Trail Integration: Connect infrastructure logs from Bifrost with quality metrics from Maxim

Comprehensive Governance Architecture

The combination of Bifrost's infrastructure controls and Maxim's quality platform creates layered governance:

Layer 1 - Infrastructure Governance (Bifrost)

• Access control and authentication
• Budget enforcement and cost management
• Rate limiting and quota management
• Audit logging and compliance trails
• Secrets management and security

Layer 2 - Quality Governance (Maxim)

• Pre-deployment evaluation and testing
• Production quality monitoring and alerting
• Agent simulation and scenario validation
• Human feedback and continuous improvement

Layer 3 - Operational Governance (Integrated)

• End-to-end observability from gateway to quality metrics
• Cross-functional collaboration workflows
• Incident response and root cause analysis
• Continuous improvement cycles

This layered approach addresses governance comprehensively without requiring organizations to stitch together multiple point solutions.

Best For

Bifrost excels for organizations that recognize infrastructure-level governance as foundational to responsible AI:

• High-Performance AI Applications where governance cannot add latency or impact user experience
• Multi-Team Organizations requiring centralized governance across engineering, product, and operations
• Regulated Industries needing comprehensive audit trails and access controls (healthcare, finance, government)
• Cost-Conscious Enterprises requiring hard budget enforcement, not just cost tracking
• Quality-Focused Teams that need pre-deployment testing and production monitoring integrated with infrastructure governance

Companies like Clinc, Thoughtful, and Atomicwork use the Bifrost-Maxim platform for comprehensive governance that doesn't compromise on performance or developer experience.

Performance Benchmarks: At 5,000 requests per second, Bifrost adds only 11µs of overhead while enforcing comprehensive governance controls. This is 50x faster than Python-based alternatives, meaning governance scales without becoming a bottleneck.

Learn More: Bifrost Documentation | Request a Demo | GitHub Repository

2. Credo AI

Platform Overview

Credo AI positions itself as a comprehensive AI governance platform focused on lifecycle management, risk assessment, and regulatory compliance. Recognized by Gartner's 2025 Market Guide for AI Governance Platforms, Credo AI provides end-to-end oversight from development through production deployment.

Key Governance Capabilities

• AI Inventory and Cataloging: Comprehensive registry of all AI use cases, models, and applications across the organization with metadata, documentation, and ownership tracking
• Risk Assessment Automation: Automated risk evaluation across AI systems including bias detection, fairness evaluation, and compliance gap identification
• Regulatory Alignment: Built-in frameworks for EU AI Act, NIST AI RMF, ISO 42001, and other regulatory standards with automated documentation generation
• Policy Management: Modular policy packs for technical, process, and documentation requirements that can be customized to organizational needs
• Model Cards and Documentation: Automated generation of model cards, impact assessments, and transparency reports for stakeholders and regulators
• Third-Party AI Governance: Assessment workflows for evaluating and governing third-party AI systems and vendors

Best For

Credo AI suits enterprises operating in heavily regulated environments that need comprehensive compliance documentation and audit readiness. Organizations in financial services, government, and healthcare benefit from Credo's focus on regulatory alignment and risk management frameworks.

3. IBM Watsonx.governance

Platform Overview

IBM Watsonx.governance extends IBM's AI platform with enterprise governance capabilities. It provides AI lifecycle governance tightly integrated with the broader Watsonx ecosystem for data and AI development.

Key Governance Capabilities

• AI Factsheets: Automated documentation of model lineage, training data, performance metrics, and governance metadata
• Drift Detection: Continuous monitoring for model drift, data drift, and fairness drift in production systems
• Bias and Fairness Monitoring: Pre-deployment and post-deployment fairness evaluation with mitigation recommendations
• Compliance Dashboard: Centralized view of compliance posture across models with risk scoring and violation tracking
• Policy Enforcement: Define and enforce governance policies at enterprise and project levels with automated checks
• Integration with MLOps: Native integration with Watsonx.ai for model development and Watsonx.data for data management

Limitations

Users note that integration with tools outside IBM's ecosystem can be challenging. The platform has a steep learning curve that may slow initial adoption. It works best for organizations already invested in IBM infrastructure.

Best For

Large enterprises with existing IBM investments that need enterprise-grade governance integrated with IBM's AI development platform. Organizations valuing established vendor relationships and comprehensive support.

4. OneTrust AI Governance

Platform Overview

OneTrust AI Governance extends OneTrust's privacy and GRC platform to AI systems. It emphasizes risk identification, compliance workflows, and integration with broader enterprise governance programs.

Key Governance Capabilities

• AI Risk Identification: Automated discovery and assessment of AI risks across the organization
• Compliance Workflow Automation: Automated linking of AI assets with regulatory requirements and compliance activities
• Geographic and Industry Customization: Compliance frameworks tailored to specific regulations (GDPR, HIPAA, etc.) and industries
• Privacy Impact Assessments: Automated generation of privacy and data protection impact assessments for AI systems
• Vendor Risk Management: Assessment and oversight of third-party AI vendors and embedded AI in SaaS applications
• Integration with GRC: Unified governance across privacy, security, and AI risk management

Limitations

The platform's focus on GRC means other governance dimensions like observability and explainability receive less attention. The tool can be complex and resource-intensive, requiring dedicated governance teams to manage effectively.

Best For

Global enterprises with complex compliance requirements across multiple jurisdictions. Organizations in highly regulated industries that need integrated privacy, security, and AI governance. Teams with established GRC practices that want to extend governance to AI systems.

5. Microsoft Azure AI

Platform Overview

Microsoft Azure AI provides governance capabilities integrated with Azure's cloud platform. For organizations deploying AI on Azure, the native governance features offer tight integration with development and deployment workflows.

Key Governance Capabilities

• Responsible AI Dashboard: Built-in tools for model debugging, fairness assessment, and error analysis
• Model Monitoring: Automated tracking of model performance, data drift, and prediction distribution
• Compliance Documentation: Automated generation of model documentation and audit trails
• Access Controls: Azure RBAC integration for fine-grained access governance
• Data Governance: Integration with Azure Purview for data lineage and compliance
• Security and Privacy: Azure security controls extended to AI workloads with encryption, network isolation, and compliance certifications

Limitations

Governance capabilities are tightly coupled to Azure infrastructure. Organizations using multi-cloud or on-premises deployments may find the platform less suitable. Feature depth in some governance areas lags specialized platforms.

Best For

Organizations standardizing on Azure for AI development and deployment. Teams wanting governance tightly integrated with development workflows. Enterprises already using Azure services that need to extend governance to AI workloads.

How to Choose the Right AI Governance Tool

Selecting AI governance tools requires mapping your specific governance needs to platform capabilities. Consider these critical dimensions:

Governance Scope and Focus

Different tools address different governance dimensions:

• Infrastructure-Level Governance: If enforcing access controls, budgets, and security at the infrastructure layer is your priority, platforms like Bifrost that operate at the AI gateway layer provide governance that cannot be bypassed. This foundational layer ensures all AI usage is governed regardless of application-level controls.
• Quality-Focused Governance: Organizations prioritizing AI output quality, accuracy, and safety benefit from platforms like Maxim that emphasize evaluation, testing, and production quality monitoring integrated with infrastructure controls
• Compliance-Focused Governance: For organizations primarily concerned with regulatory compliance and audit readiness, tools like Credo AI and OneTrust provide comprehensive compliance automation and documentation generation
• Risk-Focused Governance: If your governance program centers on risk assessment and mitigation, platforms with robust bias detection, fairness monitoring, and risk scoring should be prioritized
• Cloud-Native Governance: Teams standardizing on specific cloud platforms benefit from native governance (Azure AI, Google Vertex AI) that integrates deeply with cloud infrastructure

The fundamental question is whether to adopt infrastructure-level governance that enforces controls at the gateway layer (Bifrost approach), compliance-focused platforms that generate documentation (Credo AI approach), or cloud-native solutions tied to specific vendors (Azure AI approach). Most organizations benefit from combining infrastructure governance with compliance tools for comprehensive coverage.

Deployment and Integration Requirements

Governance tools must integrate with your AI development stack:

• Cloud vs. On-Premises: Evaluate whether tools support your deployment model. Some platforms are cloud-only. Others offer on-premises or hybrid options.
• MLOps Integration: Verify integration with your model development, deployment, and monitoring tools
• Data Platform Integration: Ensure the governance tool can access training data, feature stores, and production data sources
• Authentication and Access Control: Confirm compatibility with your identity and access management systems

Agent tracing and observability become particularly complex in distributed environments. Tools that handle multi-agent workflows and distributed tracing provide better visibility.

Team Capabilities and Resources

Governance tool adoption requires appropriate team skills and resources:

• Technical Complexity: Some platforms require extensive configuration and technical expertise. Others provide no-code interfaces for non-technical users.
• Operational Burden: Self-hosted solutions increase operational complexity. Managed platforms reduce burden but may have less flexibility.
• Support and Training: Evaluate vendor support quality, documentation completeness, and training resources
• Governance Expertise: Consider whether your team has governance specialists or needs tools that encode governance best practices

Platforms like Maxim that enable cross-functional collaboration between engineering and product teams reduce the specialized expertise barrier.

Regulatory Requirements

Your industry and geography dictate some governance tool requirements:

• Industry Compliance: Healthcare requires HIPAA compliance. Finance needs SOC 2. Government may require FedRAMP certification.
• Geographic Regulations: EU operations require GDPR compliance and likely EU AI Act preparation. U.S. federal use needs NIST AI RMF alignment.
• Audit Trail Requirements: Regulated industries often require comprehensive audit logs with specific retention periods
• Documentation Standards: Some regulations mandate specific documentation formats like model cards or data sheets

Verify that governance tools provide the compliance features and certifications your regulatory environment requires.

Cost Structure and Scalability

Governance tool costs extend beyond licensing fees:

• Licensing Model: Understand whether pricing is per user, per model, per API call, or enterprise-wide
• Implementation Costs: Factor in setup, configuration, and integration expenses
• Operational Costs: Consider ongoing maintenance, updates, and support costs
• Scaling Economics: Evaluate how costs scale as you add models, users, or API volume

How to ensure AI application reliability often requires investment in comprehensive governance. Total cost of ownership should include the cost of governance failures (regulatory fines, reputation damage, operational incidents).

Building a Comprehensive Governance Strategy

Effective AI governance combines tools, processes, and organizational practices. The most successful governance programs integrate multiple elements:

Layer Governance Tools Strategically

Effective governance requires multiple layers working together. Consider this architectural approach:

• Infrastructure Governance Layer: Start with Bifrost at the AI gateway layer for access control, budget enforcement, audit logging, and security controls that apply to all AI traffic
• Quality and Evaluation Layer: Layer Maxim's platform on top for pre-release evaluation, simulation, and production quality monitoring
• Compliance and Documentation Layer: Add tools like Credo AI for regulatory alignment, policy management, and audit trail generation when operating in heavily regulated industries
• Cloud Infrastructure Layer: Leverage cloud provider governance (Azure AI, Google Vertex AI) for deployment-level controls specific to your cloud environment

This layered approach starts with infrastructure governance as the foundation that all AI requests flow through, then adds specialized capabilities for quality assurance and compliance. The key insight is that infrastructure controls cannot be bypassed, making them the most reliable governance layer.

Embed Governance in Development Workflows

Governance cannot be an afterthought bolted onto development processes. The most effective governance integrates into existing workflows:

• Shift Left on Governance: Implement evaluation workflows that catch quality and compliance issues during development, not after deployment
• Automated Checks: Build automated governance checks into CI/CD pipelines so deployment gates enforce quality and compliance requirements
• Continuous Monitoring: Implement production monitoring that provides early warning of quality degradation or compliance drift
• Feedback Loops: Create mechanisms for incorporating production learnings into pre-deployment testing

Platforms that enable this workflow integration reduce governance friction and increase adoption.

Balance Governance and Innovation

Overly restrictive governance can stifle innovation. Underdeveloped governance creates unacceptable risk. Finding the right balance requires:

• Risk-Proportionate Controls: Implement governance appropriate to risk level. High-stakes applications need rigorous oversight. Low-risk experiments need lighter controls.
• Streamlined Processes: Design governance workflows that provide necessary oversight without introducing unnecessary delays
• Clear Guidelines: Give teams clear criteria for when governance reviews are required and what standards must be met
• Incremental Adoption: Start with governance for critical systems and expand as processes mature

Organizations that balance governance and innovation velocity gain competitive advantage while managing risk responsibly.

Measure Governance Effectiveness

Governance programs require measurement and continuous improvement:

• Coverage Metrics: Track what percentage of AI systems have governance oversight, documentation, and monitoring
• Quality Metrics: Measure AI system quality, accuracy, and safety over time to validate governance effectiveness
• Compliance Metrics: Monitor compliance posture, audit readiness, and regulatory alignment
• Efficiency Metrics: Track time from development to deployment, governance review cycle time, and process bottlenecks

Regular assessment ensures governance delivers value without becoming bureaucratic overhead.

Conclusion

AI governance in 2026 has moved from aspirational principles to operational necessity. The top governance tools address different aspects of this multifaceted challenge. Bifrost by Maxim AI uniquely combines infrastructure-level governance through the AI gateway layer with comprehensive quality assurance through the Maxim platform. Credo AI provides lifecycle governance with automated compliance. IBM Watsonx.governance offers enterprise risk management integrated with IBM's AI stack. OneTrust AI Governance specializes in GRC workflows for regulated industries. Microsoft Azure AI delivers cloud-native governance for Azure deployments.

The most effective governance strategies recognize that infrastructure controls, quality assurance, compliance documentation, and operational transparency are interconnected. Organizations need platforms that:

• Enforce infrastructure governance at the gateway layer where all AI requests flow, ensuring controls cannot be bypassed
• Enable systematic quality assurance through evaluation frameworks that validate AI behavior before and after deployment
• Provide production monitoring that detects issues quickly and enables rapid response
• Automate compliance documentation to meet regulatory requirements efficiently
• Deliver performance without overhead, ensuring governance doesn't become a bottleneck at sub-millisecond latency

For teams building AI applications at scale, Bifrost by Maxim AI provides the infrastructure governance, access controls, budget enforcement, and audit logging that form the foundation of responsible AI. Combined with Maxim's evaluation, simulation, and observability capabilities, organizations gain comprehensive governance that manages risk while enabling innovation.

The performance benchmarks tell the story: at 5,000 requests per second, Bifrost adds only 11µs of overhead while enforcing comprehensive governance controls. This is 50x faster than Python-based alternatives. Governance scales without compromise.

Ready to implement infrastructure-level AI governance? Request a demo to see how Bifrost's gateway-layer controls combined with Maxim's quality platform help teams build, deploy, and monitor AI applications with measurable quality, security, and compliance.

The future of AI governance lies not in restricting innovation but in enabling teams to ship AI systems confidently, knowing they have the infrastructure and processes to ensure quality, safety, and compliance at every layer. Choose governance tools that support this vision, and build AI systems that stakeholders can trust.