AI Governance & Compliance Services for Responsible Enterprise AI

KriraAI helps enterprises build AI systems that are safe, transparent, and compliant with global regulations. Our AI governance and compliance services combine policy design, AI risk management, and technical controls so every model stays accountable from development to production. Whether you are preparing for the EU AI Act, pursuing ISO 42001 certification, or governing generative AI across teams, we turn regulatory requirements into working systems.

22+

Industries Served

100+

Projects Delivered

Years of AI Delivery

What Is AI Governance and Compliance?

AI governance is the framework of policies, roles, processes, and technical controls that determines how an organization builds, deploys, and monitors artificial intelligence. AI compliance is the practice of proving that those AI systems meet external requirements such as the EU AI Act, ISO/IEC 42001, the NIST AI Risk Management Framework, and data protection laws like India's DPDP Act. Together, they form the operational foundation of responsible AI, where every model has a clear owner, a documented risk profile, and controls that can be audited.

The two disciplines are closely related, but they solve different problems. Governance is internal and continuous, defining who approves a model, which data it may use, and how it is monitored after launch. Compliance is external and evidence driven, converting regulatory obligations into documentation, test records, and audit trails that regulators and customers can verify.

AI Governance vs AI Compliance

Aspect

AI Governance

AI Compliance

Focus

How AI is built and managed internally

Meeting laws, standards, and contracts

Driven by

Business strategy and risk appetite

Regulators, auditors, and customers

Output

Policies, roles, approval workflows, monitoring

Evidence, documentation, audit trails

Timeline

Continuous across the AI lifecycle

Tied to regulatory and audit cycles

Why Enterprises Need AI Governance Now

AI adoption inside most enterprises has moved faster than the ability to control it. Teams deploy machine learning models and generative AI tools across departments, often without a central inventory, a risk classification, or a formal approval process. At the same time, regulators have moved from voluntary guidelines to enforceable law, which turns ungoverned AI into a legal, financial, and reputational exposure.

Regulatory penalties

Under the EU AI Act, fines for prohibited AI practices can reach EUR 35 million or 7% of global annual turnover, whichever is higher.

Bias and discrimination

Models trained on skewed data can make unfair lending, hiring, or claims decisions that harm customers and invite legal action.

Data leakage

Employees using public LLM tools can expose confidential or personal data, creating violations under GDPR and the DPDP Act.

Enterprise buyer scrutiny

Procurement and security teams increasingly ask vendors for AI governance documentation before signing contracts.

A structured AI risk management program addresses these exposures before they become incidents. It gives leadership visibility into every AI system in use and the risks each one carries. It also creates the evidence trail that regulators, auditors, and enterprise customers now expect as a baseline.

Our AI Governance & Compliance Services

KriraAI delivers end to end AI governance consulting, from the first policy draft to automated monitoring in production. Each engagement is shaped around your AI maturity, your regulatory exposure, and the systems you already run. Our AI governance solutions cover six core areas.

AI Governance Framework Design

We design an AI governance framework that fits your organization rather than a generic template. It defines AI policies, an accountable owner for each system, a cross-functional review board, and approval workflows for new use cases. The result is a clear operating model that teams can follow without slowing down delivery.

AI Risk Assessment and Risk Management

We build a complete inventory of your AI systems and classify each one by risk level, business impact, and regulatory category. Every high-risk system receives a documented AI risk assessment with mitigation controls and named owners. Ongoing AI risk management then keeps that register current as models, data, and regulations change.

AI Audit and Model Validation

Our AI audit evaluates models for bias, fairness, explainability, robustness, and performance against their intended purpose. We test with real and synthetic data, document every finding, and provide prioritized remediation steps. Audit reports are structured so they can be shared directly with regulators, auditors, or enterprise clients.

Generative AI Governance

Generative AI governance needs controls that traditional model governance does not cover. We set LLM usage policies, guardrails against hallucination and prompt injection, and safeguards that keep sensitive data out of prompts and outputs. Your teams get the productivity of generative AI without the uncontrolled risk.

AI Model Governance and Monitoring

AI model governance continues long after deployment. We implement model registries, version control, lineage tracking, and drift monitoring inside your MLOps pipeline. Alerts and dashboards flag performance drops or fairness issues before they reach customers.

AI Compliance Software Implementation

Manual spreadsheets cannot keep pace with a growing AI portfolio. We select, configure, and integrate AI compliance software and AI governance platforms, or build custom dashboards when off-the-shelf tools fall short. Evidence collection and compliance reporting become largely automated instead of a quarterly scramble.

AI Standards and Regulations We Help You Comply With

Global AI regulation is fragmented, and most enterprises face several frameworks at once. KriraAI maps your AI systems against each relevant standard and builds one unified control set that satisfies them together. This avoids duplicate work and keeps compliance manageable as new rules arrive.

EU AI Act Compliance

The EU AI Act is the first comprehensive AI law, and it applies to any company that places AI systems on the EU market, wherever that company is based. It classifies AI into prohibited, high-risk, limited-risk, and minimal-risk categories, with obligations phased in over several years. We classify your systems, close compliance gaps, and prepare the technical documentation and conformity evidence that high-risk systems require.

ISO 42001 Certification

ISO/IEC 42001 is the international standard for an AI management system (AIMS), published in December 2023. It gives organizations a certifiable structure for governing AI responsibly, similar to how ISO 27001 works for information security. We run the gap analysis, build your AIMS, and prepare your team for the ISO 42001 certification audit.

NIST AI Risk Management Framework

The NIST AI Risk Management Framework is a voluntary U.S. framework organized around four functions: Govern, Map, Measure, and Manage. It is widely adopted by enterprises and by suppliers serving regulated U.S. industries. We implement each function with practical controls and align it with ISO 42001 so one program covers both.

India: DPDP Act and AI Governance Guidelines

Indian enterprises must align AI systems with the Digital Personal Data Protection Act, 2023 and its rules, which govern how personal data is collected and processed. The India AI Governance Guidelines from MeitY add direction on accountability, transparency, and safe AI adoption. We help Indian organizations meet these requirements while staying ready for global standards as they expand abroad.

Our AI Governance Implementation

Process

Every engagement follows a five step process that moves from visibility to continuous control. Each step ends with a concrete deliverable, so progress stays measurable. Timelines depend on the size of your AI portfolio and the frameworks you target.

Discovery and AI Inventory

We identify every AI system, model, and third-party AI tool in use across the organization. Deliverable: AI System Inventory.

Gap Assessment

We compare current practices against your target regulations and standards. Deliverable: Gap Assessment Report with risk ratings.

Framework Design

We define policies, roles, controls, and approval workflows. Deliverable: AI Governance Framework and Policy Set.

Implementation

We deploy controls, configure tooling, and train your teams. Deliverable: Operational controls and trained owners.

Continuous Monitoring

We track model performance, risk, and regulatory change over time. Deliverable: Governance dashboard and periodic audit reports.

AI Governance Platforms and Tools We Work With

KriraAI is vendor neutral, so we recommend the AI governance platform that fits your stack, budget, and regulatory scope. We work across leading MLOps, model monitoring, and compliance tooling, and integrate them with your existing data and security systems. Where commercial tools leave gaps, our engineers build custom governance dashboards and automated evidence pipelines.

Industries We Serve

AI governance requirements differ sharply by industry, so our frameworks reflect sector-specific regulations from day one. KriraAI serves enterprise clients across 22+ industry verticals worldwide. Our most frequent governance engagements include the following sectors.

Why Choose KriraAI for AI Governance Consulting

Many AI governance consulting firms write policies, but few have built and deployed production AI themselves. KriraAI does both, so our governance controls are practical, technically sound, and built to work inside real engineering workflows.

Production AI Expertise

We build enterprise AI systems, so we know where risks actually appear in code, data, and pipelines.

Build Compliant, Trustworthy AI with KriraAI

AI regulation is accelerating, and enterprises that build governance now will scale AI faster and with less risk. KriraAI's AI governance services give you the framework, controls, and evidence to stay compliant across every market you serve. Start with a free assessment and get a clear, prioritized view of your AI risks and compliance gaps.

Frequently Asked Questions About AI Governance &

Compliance

AI governance is the system of policies, roles, processes, and technical controls an organization uses to manage how AI is built, deployed, and monitored. It ensures every AI system has a clear owner, a documented risk level, and ongoing oversight. Strong AI governance reduces legal, ethical, and operational risk while helping enterprises scale AI with confidence.

AI governance is how an organization internally manages its AI systems through policies, accountability, and monitoring. AI compliance is proving that those systems meet external requirements such as the EU AI Act, ISO 42001, or data protection laws. Governance is the operating model, and compliance is the evidence that the operating model meets the rules.

An AI governance framework typically includes AI policies, defined roles and accountability, an AI system inventory, risk classification, approval workflows, model documentation standards, and monitoring procedures. It also covers data governance, incident response, and regular audits. The framework should map to standards like ISO 42001 and the NIST AI RMF so one structure satisfies multiple requirements.

Yes, the EU AI Act applies to any provider or deployer whose AI system is placed on the EU market or whose AI output is used in the EU, regardless of where the company is based. This means Indian, U.S., and other non-EU companies serving European customers must comply. Penalties for the most serious violations can reach EUR 35 million or 7% of global annual turnover.

ISO 42001 certification confirms that an organization operates an AI management system meeting the ISO/IEC 42001 standard, published in December 2023. An accredited certification body audits the organization's AI policies, risk processes, and controls. Certification demonstrates responsible AI practices to customers, partners, and regulators, much like ISO 27001 does for information security.

ISO 42001 certification commonly takes three to nine months, depending on the organization's size, AI portfolio, and existing management systems. Companies already certified to ISO 27001 often move faster because many processes overlap. The timeline includes gap analysis, AI management system implementation, an internal audit, and the external certification audit.

The NIST AI Risk Management Framework is a voluntary U.S. framework focused on managing AI risks through four functions: Govern, Map, Measure, and Manage. ISO 42001 is an international, certifiable standard for an AI management system. Many enterprises use both, applying the NIST AI RMF for risk practices and ISO 42001 for a certifiable governance structure.

An AI audit reviews an AI system for bias, fairness, explainability, accuracy, robustness, security, and data quality. It also checks documentation, human oversight, and alignment with regulations and internal policies. The output is an audit report with findings, risk ratings, and remediation steps that can be shared with regulators, auditors, or enterprise customers.

Generative AI governance combines usage policies, technical guardrails, and continuous monitoring. Key controls include approved tool lists, data loss prevention for prompts, output filtering, protection against prompt injection, hallucination testing, and audit logging. The NIST Generative AI Profile and the EU AI Act rules for general-purpose AI models provide useful reference points for these controls.

Yes, Indian companies using AI to process personal data must comply with the Digital Personal Data Protection Act, 2023, which requires lawful consent, purpose limitation, data security, and accountability. The India AI Governance Guidelines from MeitY add expectations around transparency and responsible AI. Companies serving global clients also need to meet the EU AI Act and ISO 42001.