Navigating AI Governance: Compliance Strategies for Businesses

As the use of AI tools and AI technologies continues to grow across industries, the risks associated with AI have predictably multiplied. Yet, only 25% of organizations have fully implemented AI governance programs.¹ This article provides an overview of AI governance strategies your organization can implement to mitigate the risks inherent in employing artificial intelligence and deploying AI systems responsibly.

For business leaders navigating rapid AI adoption, understanding what is AI governance and how it applies across the AI lifecycle is no longer optional. Artificial intelligence governance has become a core component of risk management and regulatory compliance.

What is AI Governance?

Artificial intelligence governance concerns the policies, governance frameworks, and oversight mechanisms that determine how organizations develop, use, and deploy AI while underscoring ethical considerations, transparency, and accountability.

An effective AI governance framework helps organizations manage AI-related risks across AI development and deployment, whether they are building proprietary AI models or relying on third-party and generative AI tools. AI governance encompasses both technical and organizational controls that guide decision-making throughout the AI lifecycle.

AI governance, like other forms of technology and data governance, helps organizations identify and address risks before they lead to security incidents, regulatory violations, or reputational harm. This applies equally to startups experimenting with advanced AI and enterprises operating high risk AI systems at scale.

Effective AI governance programs typically include:

• Clear policies governing acceptable AI acceptable use
• Defined roles and accountability structures for AI oversight
• Cross-functional governance committees
• Risk assessment and continuous monitoring processes
• Integration with existing compliance, security, and privacy programs

Demonstrating strong and responsible AI governance not only supports sustainable innovation, it builds trust with customers, partners, stakeholders and regulatory bodies, while aligning AI initiatives with ethical standards and applicable AI regulations.

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Top AI Security Concerns for Businesses

AI tools are both powerful and increasingly accessible. Without robust AI governance practices, this accessibility can expose organizations to serious security, compliance, and operational risks that scale just as quickly as AI adoption itself.

Data Privacy and Protection

AI systems rely on large volumes of training data and operational data, often including sensitive, personal, or proprietary information. This creates elevated risks related to data privacy, data protection, and compliance with applicable data protection laws. A recent survey by Vanta reported that 63% of its respondents declared that data privacy and protection topped the list when it came to AI-related concerns.²

Common issues include sensitive data being used to train AI models without proper consent, personal data being retained by third-party AI providers, unclear data ownership, downstream data use, and insufficient safeguards for regulated data.

Public trust reflects these risks. In the United States, the public is far more likely to think AI will harm them (43%) than benefit them (24%), and 30% say they’re unsure of how it might affect them.³ This puts the pressure on companies that use or develop AI tools to do so in a way that builds customer trust, not suspicion by promoting trustworthy AI outcomes.

Security and Adversarial Threats

AI systems introduce new attack surfaces that traditional security controls may not fully address. Adversarial threats like prompt injection, data poisoning, and AI model manipulation can degrade AI system performance or expose sensitive information.

Threat actors increasingly use AI tools to automate phishing, generate malicious code, and bypass traditional detection mechanisms. Without AI model governance and continuous oversight, these risks can escalate quickly across AI operations.

Use of Unsanctioned AI Tools 

Shadow AI has emerged as a significant governance challenge. Employees often use unsanctioned generative AI tools without approval, unintentionally exposing confidential data, intellectual property, or customer information.

According to a 2025 UpGuard report on shadow AI, of the 1,000 employees they surveyed across the globe, 81% reported using shadow AI regularly.⁴ This increases the likelihood of data leakage, contractual violations, and loss of visibility into how AI systems are being used across the organization.

Compliance Challenges

Regulatory expectations around AI are evolving rapidly. New and proposed frameworks are increasing pressure on organizations to demonstrate responsible AI practices.

Organizations using AI must be able to do the following:

• Map AI use cases to applicable AI regulations
• Document AI risk assessments and decision-making
• Manage third-party AI vendor risks
• Demonstrate governance practices to auditors and regulators

A strong AI risk management framework enables organizations to respond confidently to audits and regulatory change.

Bias and Misinformation in AI Outputs

AI systems are not designed to distinguish truth from misinformation. Even when trained on factual data, generative AI can produce inaccurate or misleading outputs through pattern synthesis.⁵

AI systems may also reinforce bias or produce outcomes that conflict with legal and ethical boundaries. This is especially concerning in high-impact use cases such as hiring, lending, healthcare, and customer communications. Research has shown persistent bias in AI-driven HR systems, contributing to discriminatory hiring outcomes.⁶ 

Despite these risks, AI adoption in HR continues to grow, with 43% of organizations now using AI in HR-related tasks.⁷ This underscores why AI ethics and governance must be embedded into AI development and deployment decisions.

Key AI Governance Strategies for Organizations

Implementing AI governance does not mean limiting innovation. It enables safe, scalable, and responsible AI adoption. The most effective AI governance frameworks combine policy, process, and technical controls across the AI lifecycle.

Establish a Responsible AI and Ethics Framework

At the core of AI governance are clearly defined AI governance principles. These typically include fairness, transparency, accountability, human oversight, and responsible AI practices.

ISO 42001 provides a structured governance framework for implementing and managing AI systems while minimizing AI risks and unintended consequences.

Access BD Emerson’s Comprehensive ISO 42001 Implementation Guide

Implement AI Use Case Inventory and Risk Classification

Organizations cannot govern what they cannot see. Maintaining an inventory of AI systems and AI use cases across the business is foundational to effective AI governance.

Each use case should be classified based on data sensitivity, impact on individuals, degree of automation, and regulatory exposure. This enables appropriate security controls and oversight mechanisms based on risk.

Integrate AI Governance with Existing Compliance Programs

AI governance should align with existing compliance and data governance programs. Integrating AI governance policies into privacy, security, third-party risk management, and audit processes reduces duplication and strengthens overall risk management.

Enforce Data Governance and Access Controls for AI

When consumer AI tools entered the market, many companies didn’t realize their own data could be at risk if employees or partners entered sensitive information into insecure AI outputs.

It’s evident that clear rules around data usage in AI systems are essential. Controls should include restrictions on sensitive data use, role-based access to AI platforms, monitoring data flows, and enforcing data minimization and retention limits.

These measures reduce the risk of data leakage, regulatory noncompliance, and unintended model training.

Strengthen Third-Party and Vendor AI Oversight

Most organizations rely on external AI vendors. AI governance must extend to vendor risk management, including assessments of vendor AI governance practices, transparency into model behavior, and contractual controls around data use and retention.

Monitor, Audit, and Continuously Improve AI Systems

AI governance is an ongoing process. Models, tools, and risks evolve over time, making continuous monitoring essential.

Here are key tips for monitoring your company’s AI tools: 

• Regularly review AI performance and outputs
• Monitor for bias, drift, and security issues
• Conduct periodic audits of AI use and compliance
• Update governance controls as regulations and technologies change

Continuous improvement helps organizations stay ahead of emerging risks and regulatory expectations.

The Benefits of AI Governance Programs

A comprehensive AI governance program delivers measurable business value beyond compliance. For startups, AI governance establishes trust early and supports scalable growth. For enterprises, it enables consistent oversight across complex AI environments.

Key benefits include:

• Reduced AI risks through structured oversight and monitoring
• Improved regulatory compliance with evolving AI regulations such as the EU AI Act
• Stronger data protection and data quality controls
• Increased transparency and accountability in AI outcomes
• Alignment of AI initiatives with ethical standards and business objectives
  Greater customer and stakeholder trust in AI systems

Effective AI governance ensures that AI systems operate responsibly, that AI developers and business teams share collective responsibility, and that AI innovation supports long-term business resilience.

How BD Emerson Can Bolster Your AI Governance Program

BD Emerson’s AI governance consulting helps organizations design and implement AI governance frameworks that align with regulatory requirements, ethical standards, and business objectives.

Our services support responsible AI governance across the full AI lifecycle, from policy development to ongoing monitoring and incident response.

Customized AI Governance Solutions for Diverse Frameworks

BD Emerson's AI governance solutions are tailored strategies that align with your unique business needs, including those required by existing security frameworks and compliance obligations. Our AI governance experts understand that each organization has its own specific challenges and requirements, and our solutions are designed with these in mind. 

In addition to GDPR, HIPAA, NIST,  SOC 2 and ISO 27001, we also offer consulting for the following AI-focused frameworks:

• ISO 42001 – an international standard that specifies requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS) within organizations⁸
• EU AI Act – a law that governs the development and/or use of artificial intelligence in the European Union 
• NIS 2 – a standard provides an agile cybersecurity foundation that equips organizations with risk-awareness strategies, easily applicable to AI-related risks and concerns
• DORA – (EU Digital Operational Resilience Act) establishes a risk and security framework to ensure that banks and other financial institutions can safely rely on digital service providers to maintain market stability

Learn about BD Emerson’s AI Governance Consulting

On-Demand Support of AI tools and Strategies

BD Emerson’s versatile AI governance services can serve your organization in place of an AI governance company, overseeing the implementation and running of a customized AI governance approach. Our consultants are available 24/7 to advise on the security AI tools and third-party AI integrations. 

Rapid AI Incident Response

In the event of a security incident, BD Emerson’s team assists clients in reporting the event, performing remediation actions, and ensuring a clear audit trail of conformity assessments and monitoring activities. 

We’ll Help You Build an AI Governance Program Tailored to Your Business

AI adoption is accelerating. Organizations without an effective AI governance framework are exposed to increasing legal, security, and reputational risks.

BD Emerson helps organizations implement robust, responsible AI governance programs that support innovation while protecting stakeholders.

Talk to our AI Governance experts today

References

1. The 20 Biggest AI Governance Statistics and Trends of 2025, Knostic: https://www.knostic.ai/blog/ai-governance-statistics 

2. Understanding AI governance: Why most organizations feel overwhelmed by regulations, Vanta: https://www.vanta.com/resources/ai-governance
3. How the U.S. Public and AI Experts View Artificial Intelligence, Pew Research Center:https://www.pewresearch.org/internet/2025/04/03/how-the-us-public-and-ai-experts-view-artificial-intelligence/ 
4. The State of Shadow AI, UpGuard: https://www.upguard.com/resources/the-state-of-shadow-ai 
5. “When A.I. Chatbots Hallucinate,” The New York Times: https://www.nytimes.com/2023/05/01/business/ai-chatbots-hallucination.html 
6. Bias in AI-driven HRM systems: Investigating discrimination risks embedded in AI recruitment tools and HR analytics. Science Direct: https://www.sciencedirect.com/science/article/pii/S2590291125008113#bib35 
7. 2025 Talent Trends: AI in HR. SHRM.org: https://www.shrm.org/topics-tools/research/2025-talent-trends/ai-in-hr 
8. ISO/IEC 42001:2023. ISO: https://www.iso.org/standard/42001
9. With generative AI, social engineering gets more dangerous—and harder to spot. IBM: https://www.ibm.com/think/insights/generative-ai-social-engineering 
10. Working on ISO 27001? It’s Time to Add ISO 42001 to Your Strategic Plan. BD Emerson: https://www.bdemerson.com/article/working-on-iso-27001-its-time-to-add-iso-42001-to-your-strategic-plan