Artificial Intelligence Governance, Risk & Compliance (AI GRC)
Artificial Intelligence Governance, Risk & Compliance (AI GRC)
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Let's be direct about where we are with AI regulation: the window for "we'll figure out governance later" has closed.
The EU AI Act — the world's first comprehensive legal framework for artificial intelligence — is already in force, with high-risk AI system obligations taking effect on a rolling basis. Financial regulators in the US, UK, and India are issuing AI-specific guidance that affects algorithmic decision-making in lending, insurance, and trading. Healthcare regulators are scrutinizing AI-assisted diagnostics and clinical decision support. And organizations in every sector are discovering that their customers, partners, boards, and insurers are asking questions about AI governance that nobody prepared answers for.
Meanwhile, the internal risks of ungoverned AI deployment are compounding quietly. Shadow AI — employees using unauthorized AI tools to process sensitive data — is a data breach waiting to happen. Automated decisions made by AI systems with no documented accountability trail are a liability exposure. Models trained on biased data are producing outputs that create legal and reputational risk. And AI systems deployed without security review are introducing vulnerabilities that traditional security programs aren't designed to catch.
Only 24% of organizations have fully enforced enterprise AI GRC policies, according to recent research. That means three out of four organizations — including many of your competitors — are running AI deployments that aren't governed, aren't audited, and aren't ready for regulatory scrutiny.
InTechsters helps organizations close that gap. We build practical, audit-ready AI governance frameworks that enable responsible AI deployment — protecting your organization from regulatory risk, operational exposure, and reputational harm while positioning you to use AI with confidence.
Our AI GRC Services
Our AI GRC Services
AI Risk Assessment & Inventory
AI Risk Assessment & Inventory
AI System Inventory — You can't govern what you don't know exists. We conduct a comprehensive inventory of all AI systems in your organization — deployed, in development, and in use by employees (shadow AI) — covering model type, data inputs, decision outputs, business function, and risk profile.
AI System Inventory — You can't govern what you don't know exists. We conduct a comprehensive inventory of all AI systems in your organization — deployed, in development, and in use by employees (shadow AI) — covering model type, data inputs, decision outputs, business function, and risk profile.
AI Risk Assessment — Structured evaluation of each AI system for risk across multiple dimensions: regulatory compliance, data privacy, security, bias and fairness, explainability, operational reliability, and reputational exposure. Risk ratings inform governance priority and control requirements.
AI Risk Assessment — Structured evaluation of each AI system for risk across multiple dimensions: regulatory compliance, data privacy, security, bias and fairness, explainability, operational reliability, and reputational exposure. Risk ratings inform governance priority and control requirements.
Shadow AI Discovery — Identifying unauthorized AI tool usage across your organization — employees using consumer AI tools (ChatGPT, Gemini, Copilot, Midjourney) to process sensitive business data without appropriate controls or approved channels.
Shadow AI Discovery — Identifying unauthorized AI tool usage across your organization — employees using consumer AI tools (ChatGPT, Gemini, Copilot, Midjourney) to process sensitive business data without appropriate controls or approved channels.
Third-Party AI Risk Assessment — Evaluating the AI components embedded in your vendor and SaaS products — CRM systems with AI features, AI-powered security tools, automated decision systems — for governance gaps and contractual accountability.
Third-Party AI Risk Assessment — Evaluating the AI components embedded in your vendor and SaaS products — CRM systems with AI features, AI-powered security tools, automated decision systems — for governance gaps and contractual accountability.
AI Risk Register — Maintaining a living risk register for your AI portfolio — documenting identified risks, risk owners, treatment decisions, control effectiveness, and residual risk across all AI systems.
AI Risk Register — Maintaining a living risk register for your AI portfolio — documenting identified risks, risk owners, treatment decisions, control effectiveness, and residual risk across all AI systems.
AI Governance Framework Design
AI Governance Framework Design
AI Governance Policy Development — Creating your foundational AI governance policies — Acceptable Use Policy for AI, AI Development Policy, AI Procurement Policy, AI Data Governance Policy, and AI Ethics Policy — tailored to your industry, regulatory environment, and organizational culture.
AI Governance Policy Development — Creating your foundational AI governance policies — Acceptable Use Policy for AI, AI Development Policy, AI Procurement Policy, AI Data Governance Policy, and AI Ethics Policy — tailored to your industry, regulatory environment, and organizational culture.
AI Governance Structure — Establishing the governance structures that make AI policy effective — AI governance committees, review and approval workflows for new AI deployments, escalation paths for AI-related incidents, and clear accountability frameworks for AI decision-making.
AI Governance Structure — Establishing the governance structures that make AI policy effective — AI governance committees, review and approval workflows for new AI deployments, escalation paths for AI-related incidents, and clear accountability frameworks for AI decision-making.
AI Ethics Framework — Defining your organization's principles for ethical AI use — fairness, transparency, accountability, human oversight, and non-maleficence — and operationalizing those principles into concrete review criteria and decision gates.
AI Ethics Framework — Defining your organization's principles for ethical AI use — fairness, transparency, accountability, human oversight, and non-maleficence — and operationalizing those principles into concrete review criteria and decision gates.
AI Roles & Responsibilities — Defining who is accountable for AI governance across your organization — AI owners, data stewards, risk officers, compliance leads, and technical reviewers — with clear responsibility matrices.
AI Roles & Responsibilities — Defining who is accountable for AI governance across your organization — AI owners, data stewards, risk officers, compliance leads, and technical reviewers — with clear responsibility matrices.
Human Oversight Requirements — Establishing requirements for human review and override capabilities across AI systems — particularly for automated decisions with significant consequences for individuals or the business.
Human Oversight Requirements — Establishing requirements for human review and override capabilities across AI systems — particularly for automated decisions with significant consequences for individuals or the business.
AI Regulatory Compliance
AI Regulatory Compliance
EU AI Act Compliance — Assessment and compliance roadmap for the EU AI Act, including AI system classification (unacceptable risk, high risk, limited risk, minimal risk), conformity assessment requirements for high-risk systems, technical documentation, transparency obligations, and registration requirements.
EU AI Act Compliance — Assessment and compliance roadmap for the EU AI Act, including AI system classification (unacceptable risk, high risk, limited risk, minimal risk), conformity assessment requirements for high-risk systems, technical documentation, transparency obligations, and registration requirements.
NIST AI RMF Implementation — Implementation of NIST's AI Risk Management Framework across your AI portfolio — governing, mapping, measuring, and managing AI risks using the industry's leading voluntary framework.
NIST AI RMF Implementation — Implementation of NIST's AI Risk Management Framework across your AI portfolio — governing, mapping, measuring, and managing AI risks using the industry's leading voluntary framework.
ISO/IEC 42001 Readiness & Certification — Gap assessment and implementation support for ISO/IEC 42001 — the world's first AI management system standard — for organizations seeking formal certification of their AI governance practices.
ISO/IEC 42001 Readiness & Certification — Gap assessment and implementation support for ISO/IEC 42001 — the world's first AI management system standard — for organizations seeking formal certification of their AI governance practices.
Sector-Specific AI Compliance — Compliance guidance for AI-specific regulatory requirements in:
Sector-Specific AI Compliance — Compliance guidance for AI-specific regulatory requirements in:
Financial Services — RBI, SEBI, SEC, FCA, and other financial regulator guidance on algorithmic trading, automated credit decisions, and AI-driven fraud detection
Healthcare — FDA guidance on AI/ML-based Software as a Medical Device (SaMD), CDSCO requirements, HIPAA implications of AI systems processing health data
Insurance — Algorithmic fairness requirements and explainability obligations for AI-driven underwriting and claims decisions
Government & Public Sector — Responsible AI requirements for public-sector AI deployment, algorithmic accountability, and transparency obligations
GDPR & DPDPA Compliance for AI — Assessment of AI systems for compliance with data protection regulations — lawful basis for AI training data, data subject rights in automated decision-making (Article 22 GDPR), privacy impact assessments for AI systems, and data minimization in model training.
GDPR & DPDPA Compliance for AI — Assessment of AI systems for compliance with data protection regulations — lawful basis for AI training data, data subject rights in automated decision-making (Article 22 GDPR), privacy impact assessments for AI systems, and data minimization in model training.
AI Transparency & Explainability Requirements — Implementing the disclosures, explanations, and audit trails required by regulations — including automated decision notices, right to explanation mechanisms, and model documentation for regulatory review.
AI Transparency & Explainability Requirements — Implementing the disclosures, explanations, and audit trails required by regulations — including automated decision notices, right to explanation mechanisms, and model documentation for regulatory review.
AI Risk Controls & Safeguards
AI Risk Controls & Safeguards
Data Governance for AI — Establishing controls for the data used to train, fine-tune, and operate AI systems — data provenance documentation, training data quality assessment, bias detection and mitigation, sensitive data handling, and data retention for AI training datasets.
Data Governance for AI — Establishing controls for the data used to train, fine-tune, and operate AI systems — data provenance documentation, training data quality assessment, bias detection and mitigation, sensitive data handling, and data retention for AI training datasets.
Bias & Fairness Assessment — Evaluating AI systems for discriminatory outputs across protected characteristics — race, gender, age, religion, disability — using statistical fairness metrics and demographic parity analysis, with remediation guidance for identified bias.
Bias & Fairness Assessment — Evaluating AI systems for discriminatory outputs across protected characteristics — race, gender, age, religion, disability — using statistical fairness metrics and demographic parity analysis, with remediation guidance for identified bias.
Model Documentation & Model Cards — Creating standardized model documentation — model cards, datasheets for datasets, and system cards — that describe model purpose, training data, performance metrics, known limitations, and appropriate use cases.
Model Documentation & Model Cards — Creating standardized model documentation — model cards, datasheets for datasets, and system cards — that describe model purpose, training data, performance metrics, known limitations, and appropriate use cases.
AI Incident Management — Building AI-specific incident response processes for managing AI failures — model performance degradation, biased output incidents, security compromises of AI systems, and regulatory notifications related to AI system failures.
AI Incident Management — Building AI-specific incident response processes for managing AI failures — model performance degradation, biased output incidents, security compromises of AI systems, and regulatory notifications related to AI system failures.
AI Change Management — Governance controls for AI model updates — review and approval requirements for model retraining, version control for AI models, rollback procedures, and post-deployment monitoring requirements.
AI Change Management — Governance controls for AI model updates — review and approval requirements for model retraining, version control for AI models, rollback procedures, and post-deployment monitoring requirements.
AI Access Controls — Authorization frameworks for who can access, modify, deploy, and retire AI systems — with privileged access management for model weights, training pipelines, and inference infrastructure.
AI Access Controls — Authorization frameworks for who can access, modify, deploy, and retire AI systems — with privileged access management for model weights, training pipelines, and inference infrastructure.
AI Auditing & Assurance
AI Auditing & Assurance
AI Audit Program Design — Building an internal AI audit capability — audit scope, methodology, frequency, evidence requirements, and reporting structure for ongoing assurance over your AI portfolio.
AI Audit Program Design — Building an internal AI audit capability — audit scope, methodology, frequency, evidence requirements, and reporting structure for ongoing assurance over your AI portfolio.
AI System Audit — Conducting formal audits of specific AI systems — evaluating compliance with internal policies, external regulations, and ethical commitments, with documented findings and remediation requirements.
AI System Audit — Conducting formal audits of specific AI systems — evaluating compliance with internal policies, external regulations, and ethical commitments, with documented findings and remediation requirements.
AI Audit Trail & Explainability — Implementing logging and audit trail capabilities that record AI system inputs, outputs, decision rationale, and model versions — creating the evidence base needed for regulatory inquiry, internal investigation, and accountability.
AI Audit Trail & Explainability — Implementing logging and audit trail capabilities that record AI system inputs, outputs, decision rationale, and model versions — creating the evidence base needed for regulatory inquiry, internal investigation, and accountability.
Pre-Deployment AI Review — Structured review and approval process for new AI deployments — evaluating risk classification, governance requirements, security assessment status, data protection compliance, bias assessment, and human oversight adequacy before go-live.
Pre-Deployment AI Review — Structured review and approval process for new AI deployments — evaluating risk classification, governance requirements, security assessment status, data protection compliance, bias assessment, and human oversight adequacy before go-live.
Continuous AI Monitoring — Ongoing monitoring of deployed AI systems for performance drift, bias emergence, data distribution shift, adversarial attacks, and compliance with governance policies.
Continuous AI Monitoring — Ongoing monitoring of deployed AI systems for performance drift, bias emergence, data distribution shift, adversarial attacks, and compliance with governance policies.
Regulatory Examination Support — Preparing for and supporting regulatory examinations that include AI governance — evidence collection, examiner liaison, gap remediation, and post-examination remediation management.
Regulatory Examination Support — Preparing for and supporting regulatory examinations that include AI governance — evidence collection, examiner liaison, gap remediation, and post-examination remediation management.
AI Vendor & Procurement Governance
AI Vendor & Procurement Governance
AI Vendor Due Diligence — Security, privacy, and governance assessment of AI vendors and AI-powered SaaS products before procurement — evaluating model transparency, data handling practices, bias testing, security posture, and contractual accountability.
AI Vendor Due Diligence — Security, privacy, and governance assessment of AI vendors and AI-powered SaaS products before procurement — evaluating model transparency, data handling practices, bias testing, security posture, and contractual accountability.
AI Contract Review — Advisory on AI-specific contract terms — data ownership, model training on customer data, explainability obligations, liability for AI errors, audit rights, regulatory compliance warranties, and AI incident notification requirements.
AI Contract Review — Advisory on AI-specific contract terms — data ownership, model training on customer data, explainability obligations, liability for AI errors, audit rights, regulatory compliance warranties, and AI incident notification requirements.
AI Procurement Policy — Establishing a formal AI procurement policy with approved AI tool categories, prohibited uses, vendor qualification requirements, and ongoing vendor governance obligations.
AI Procurement Policy — Establishing a formal AI procurement policy with approved AI tool categories, prohibited uses, vendor qualification requirements, and ongoing vendor governance obligations.
Foundation Model Risk Assessment — Evaluating the risks of relying on third-party foundation models — model provider concentration risk, API dependency, data handling by model providers, and the governance implications of using models you don't control.
Foundation Model Risk Assessment — Evaluating the risks of relying on third-party foundation models — model provider concentration risk, API dependency, data handling by model providers, and the governance implications of using models you don't control.
Frameworks & Standards We Implement
Frameworks & Standards We Implement
EU AI Act — Comprehensive EU regulation for AI systems
EU AI Act — Comprehensive EU regulation for AI systems
NIST AI Risk Management Framework (AI RMF 1.0) — US voluntary AI risk framework
NIST AI Risk Management Framework (AI RMF 1.0) — US voluntary AI risk framework
ISO/IEC 42001:2023 — AI management system standard
ISO/IEC 42001:2023 — AI management system standard
ISO/IEC 23894 — AI risk management guidance
ISO/IEC 23894 — AI risk management guidance
MITRE ATLAS — Adversarial threat landscape for AI
MITRE ATLAS — Adversarial threat landscape for AI
OECD AI Principles — International AI governance principles
OECD AI Principles — International AI governance principles
India NITI Aayog AI Framework — India's responsible AI guidance
India NITI Aayog AI Framework — India's responsible AI guidance
Sector-specific regulatory guidance — RBI, SEBI, CDSCO, FDA, FCA, SEC
Sector-specific regulatory guidance — RBI, SEBI, CDSCO, FDA, FCA, SEC
Frequently Asked Questions
Frequently Asked Questions
We're a mid-sized company. Does AI governance apply to us?
If you're deploying AI — or your vendors are deploying AI that affects your customers or employees — governance applies to you regardless of size. Regulatory obligations under the EU AI Act, GDPR, and sector-specific regulations don't have size exemptions. And the operational and reputational risks of ungoverned AI deployment don't either.
We only use AI tools from established providers like Microsoft or Google. Do we still need AI GRC?
Yes. When you deploy AI tools — even from established providers — you become responsible for how those tools are used in your organization, what data is processed through them, what decisions they influence, and what disclosures you make to affected individuals. Provider governance doesn't substitute for your own.
What's the difference between AI GRC and AI security testing?
AI security testing (AI VAPT) evaluates the technical vulnerabilities in your AI systems — prompt injection, model extraction, adversarial attacks. AI GRC evaluates the governance, risk management, and compliance framework around your AI portfolio — policies, accountability, regulatory compliance, bias assessment, audit trails. Both are necessary. Security testing without governance is incomplete; governance without security testing has blind spots.
How do we start if we haven't done anything on AI governance yet?
Start with an AI inventory and risk assessment. Before you can govern AI, you need to know what AI systems you have, who owns them, what data they process, and what decisions they influence. We help you build that foundation and prioritize governance efforts based on risk.
Is the EU AI Act relevant if we're based in India?
If your AI systems process data about EU residents, affect EU-based customers, or if your products are sold in the EU market, the EU AI Act applies to you regardless of where you're headquartered. Many Indian technology companies and exporters have EU exposure that makes EU AI Act compliance relevant.
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