AI governance establishes the principles, roles, processes, and controls for responsible AI deployment. It transforms abstract ethics into concrete practices.

Think of ​​AI governance as a rulebook for how to use AI in a secure, ethical, observable, moderated, and auditable fashion. It’s a systematic framework of policies and practices that governs the deployment, management, and optimization of AI data and technology within an organization. 

Strong AI governance measures are designed to guarantee that AI systems are utilized legally and ethically, putting safeguards in place for the organization. This way companies can manage risks and be performant at the same time.

AI governance is broader than just ethical concerns. While ethical considerations are a core component, AI governance is essentially everything a company will need to consider if they want to implement AI throughout the company's different functions

AI governance use cases and examples

Not long ago, AI governance was just theory. Now, it’s rapidly shaping industries. Here’s how different sectors are tackling AI challenges.

AI in healthcare

AI has sent shockwaves through the healthcare industry, especially when it comes to handling patient care. However, due to the sensitive nature of patient data, which is a top priority for governing bodies, it requires strict oversight. Compliance measures like HIPAA are in place to ensure privacy and security for patients, and AI governance plays a big role in keeping data HIPAA-compliant. It also helps prevent bias in AI diagnostic tools so that all patients receive accurate and fair assessments. Finally, AI governance makes AI decisions more explainable, which is crucial because doctors and medical professionals need transparency to know they can trust AI-driven insights.

AI in finance

The financial sector relies on AI for critical functions like credit scoring, fraud detection, and risk management, but they must abide by fair lending practices as set by government regulations. AI governance requires AI models to be trained on diverse datasets to prevent discrimination and follow a clear line of decision-making to enable customers to understand why they are approved or denied credit. Furthermore, financial institutions must adhere to regulatory requirements, such as the Fair Credit Reporting Act, to maintain compliance and ethical standards.

AI in retail

AI has greatly enhanced personalized shopping experiences for customers and increased sales for many companies. AI governance helps to maintain consumer trust by ensuring data privacy policies so that customers know how their data is being collected and used by the retail company. AI also helps retailers avoid bias with AI recommendations, ensuring that product suggestions and promotions do not unfairly favor certain groups over others.

When thinking about your company's AI Governance strategy, it’s vital to understand the existing NIST, ISO, and EU standards. 

The NIST AI Risk Management Framework (AI RMF)

Released on January 26, 2023, AI RMF was developed through a consensus-driven, open, transparent, and collaborative process that included a Request for Information, several draft versions for public comments, multiple workshops, and other opportunities to provide input (AI Risk Management Framework | NIST). The framework uses four core functions:

• Govern: Builds risk management culture and clear processes
• Map: Identifies AI risks in specific contexts
• Measure: Assesses and tracks identified risks
• Manage: Implements and monitors control effectiveness

Federal agencies are adopting these practices as part of broader AI governance initiatives. Private sector adoption follows rapidly.

ISO/IEC 42001

ISO/IEC 42001 is an international standard that specifies requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS) within organizations (ISO/IEC 42001:2023 - AI management systems). This standard creates an auditable management system for AI. Organizations implement it to demonstrate governance maturity through third-party certification. The standard offers:

• Certifiable proof of governance maturity
• Streamlined audits for multi-certified organizations
• International recognition and acceptance
• Clear commitment demonstration to stakeholders

Major enterprises are pursuing certification, with industry leaders already achieving this milestone.

EU AI Act Requirements

The Act categorizes AI into four risk tiers:

• Prohibited: Unacceptable risk systems (banned outright)
• High-Risk: Critical application areas (strict requirements)
• Limited-Risk: Transparency obligations (disclosure required)
• Minimal-Risk: No specific obligations

The EU AI Act entered into force on 1 August 2024. Two critical deadlines have now passed: prohibited AI systems were banned in February 2025, and General-Purpose AI (GPAI) transparency requirements became mandatory in August 2025 (EU AI Act Compliance Timeline | Trilateral Research).

Why AI Governance Matters Now

Three forces drive AI governance to the top of executive agendas:

Regulatory Pressure Intensifies
From February 2025, some AI systems are banned and AI literacy becomes mandatory (Implementation Timeline | EU Artificial Intelligence Act). Rules for general-purpose AI apply from August 2, 2025. Member States must designate national competent authorities. Penalties reach EUR35 million or 7% of global annual turnover for prohibited practice violations (EU AI Act | DLA Piper). The U.S. Federal Trade Commission already prosecutes undisclosed biased models as "unfair and deceptive."

Risk Management Becomes Critical
One in five organizations reported a breach due to shadow AI. Only 37% have policies to manage AI or detect shadow AI. Organizations using high levels of shadow AI observed an average of $670,000 in higher breach costs (IBM Report: Reported Breaches of AI Models). Model failures damage reputations instantly. Traditional risk frameworks miss AI's unique challenges—from drift to adversarial attacks.

Trust Creates Competitive Advantage
Public trust in AI is critical for adoption and business success. Organizations demonstrating responsible AI win contracts, attract talent, and build customer loyalty. Early compliance establishes market leadership.

Critical Compliance Timeline

Mark these dates:

2025:

• February 2025: Prohibitions on certain AI systems and AI literacy requirements apply
• August 2025: Rules for general-purpose AI apply. Governance must be in place. Member States designate national competent authorities 

2026-2027:

• August 2026: Rules for high-risk AI systems in Annex III enter into application (Timeline for the Implementation | AI Act Service Desk)
• August 2027: Pre-August 2025 GPAI model providers must comply with AI Act obligations (EU AI Act - Updates, Compliance, Training)

While the U.S. has not established a single federal AI governance mandate for April 2025, recent executive actions focus on preventing state-level regulatory fragmentation and establishing national AI policy frameworks. Global alignment continues to evolve.

Ethical considerations

Ethical considerations make up the backbone of AI governance to ensure that systems are transparent, accountable, secure, and unbiased.

• Fairness and non-discrimination: AI should work for everyone, not just the select few who built it. Preventing bias means using diverse training data, consistently running bias tests, and fixing issues before they impact real users.
• Transparency: Transparent, explainable AI models allow users to better understand AI decisions and use the technology. Using decision trees or tools like LIME and SHAP can help make AI more transparent and easier to oversee.
• Accountability: Definitive governance structures assign individuals or teams to oversee system performance, enforce audits, and address threats or concerns. This ensures that parties are accountable to keep the AI functional and effective.  
• Privacy and data protection: AI processes vast amounts of sensitive data. Strict governance protocols, like secure storage, data minimization, and privacy-first design, keep personal or private information safe and compliant.

Legal and regulatory frameworks

Another concern that goes hand-in-hand with ethics is compliance. Legal and regulatory frameworks like the GDPR (EU) and CCPA (US) set strict rules on the collection, management, and protection of personal and sensitive data. Organizations must justify data use, obtain clear consent, and enforce strong security measures.

Additionally, new laws are emerging to keep pace with AI advancements. The EU Artificial Intelligence Act and US Algorithmic Accountability Act push for greater transparency and accountability, placing restrictions on high-risk AI applications like facial recognition and automated decision-making.

Beyond national laws, global standards like the OECD AI Principles and IEEE Ethically Aligned Design provide ethical blueprints for AI governance. Aligning with these frameworks ensures compliance, builds trust, and promotes a unified global approach to responsible AI.

Risk management

There are certainly risks tied to the development and deployment of AI systems, and strong risk management protocols must be adopted in AI governance to identify, evaluate, and mitigate these risks.

• Identification: The first step in any risk management process is to identify potential dangers and bad actors, such as data privacy breaches, algorithmic bias, lack of transparency, or AI misuse. Risk assessments should consider the system’s unique context, application, and broad impact. 
• Mitigation: Once risks to the AI system have been identified, organizations must take action to prevent or quickly respond to them. This means strengthening data protection, implementing techniques like access control, and setting clear policies for AI development and deployment. These strategies should evolve as new risks and environments emerge.
• Monitoring and review: Risk management doesn’t stop after deployment. AI systems need constant auditing, performance tracking, and stakeholder feedback to catch new risks early and get ahead of progress. Clear escalation and resolution protocols and transparent reporting are critical parts of the review process. 

Stakeholder engagement

There have been concerns about how AI will impact individuals, businesses, and society at large. Because of this, governance must be a collective effort.

• Collaboration: Effective AI governance requires teamwork across governments, industries, academia, and civil society. Multi-stakeholder forums and public-private partnerships help create balanced, inclusive policies that address diverse concerns and opportunities in the AI sector.
• Public participation: AI governance often benefits from democratic processes, such as public input through consultations and feedback channels, either internally or externally. Such measures may help align AI systems with societal values, build trust, and uncover insights that even experts might miss.
• Stakeholder interests: As with any other endeavor, AI governance must also strike a balance with stakeholders. This includes combining innovation and safety, benefits and risks, and goals and needs. A well-balanced approach can drive progress while keeping ethics at the forefront of concerns.

AI governance in development practices

AI governance can play a part in many common modern development practices.

• AIOps: AI-driven IT operations (AIOps) require strong governance to maintain maximum efficiency and compliance. Real-time monitoring can detect anomalies, trigger alerts, and automate remediation actions, while clear policies streamline incident response, root cause analysis, and continuous improvement.
• DevOps: Developers shouldn’t bypass integrating ethical AI practices into development operations (DevOps). Automated testing for bias, fairness, and transparency can be embedded into the continuous integration and delivery (CI/CD) pipeline using tools like IBM's AI Fairness 360 or Google's What-If Tool. These methods help developers enhance model transparency by generating human-readable explanations of model decisions and highlighting any areas of ambiguity.

As AI becomes more prevalent across all industries, developers will need to understand how to deploy governance for ethical data use, risk mitigation, and transparency. Read on to learn how AI governance functions in different stages of the AI lifecycle for development. 

• Data management: High-quality data is the bedrock of trustworthy AI. Governance practices such as data quality control, lineage tracking, secure data storage, and access management help ensure that data used for AI training and inference is accurate, complete, and consistent. Plus, encryption and role-based access control protect sensitive data from unauthorized access or breaches.
• Model development: In order to develop fair and unbiased AI models, there needs to be diverse training data, regular bias testing, and thorough documentation. Techniques like disparate impact analysis and sensitivity analysis help assess fairness by detecting bias and discrimination. The model development process should also include documentation of model architecture, training data, and evaluation metrics.
• Explainable AI: Transparency can't be overstated when it comes to establishing user trust and effective human oversight. AI models should provide clear explanations of decisions with feature importance scores or decision trees, and techniques like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) can generate post-hoc explanations for black-box models. By making the AI model more intuitive and explainable, organizations can enable better decision-making and improve trust in AI outputs.
• AI security: Protecting AI systems from attacks is of utmost importance to maintain the integrity and confidentiality of AI data. Robust security measures like adversarial training defend against data poisoning and adversarial attacks from bad actors, as well as model stealing attacks, which attempt to extract the underlying model parameters or training data. Encryption, access controls, and regular security audits and penetration testing also help safeguard AI models by identifying and addressing any vulnerabilities in AI systems.
• AI testing and validation: No development work is complete without testing and validation. Every AI system needs regular, comprehensive performance testing to check its quality and reliability. Performance testing, bias audits, and user acceptance testing all validate AI outputs to protect user groups and ensure the AI meets real-world needs.
• AI lifecycle management: Governance must span the entire AI lifecycle, from design and development to deployment and retirement. This helps verify the system’s compliance with evolving regulations and standards. There should be clear policies and procedures for each stage of the AI lifecycle, including data collection, model validation, monitoring, and archiving. Organizations should also implement mechanisms for tracking and auditing compliance with specific relevant regulations and standards, such as GDPR or IEEE's Ethically Aligned Design. Taking a holistic approach to AI lifecycle management means organizations can ensure their AI remains safe, effective, and legally compliant.

To truly grasp the impact of AI governance, let’s delve into a real-world example from the financial sector. Goldman Sachs, a stalwart in the investment banking industry, has been pioneering the use of AI and APIs to drive business success and enhance client services. This case showcases the practical implications of robust AI governance, emphasizing transparency, compliance, and innovation.

Goldman Sachs: A Case Study in Effective AI Governance

Goldman Sachs has long been a leader in adopting cutting-edge technology to maintain its competitive edge. At the forefront of their strategy is an API-first approach, which has been integral to their operations for over a decade. Rohan Deshpande, Managing Director at Goldman Sachs, highlights how APIs serve as the backbone of their technological framework, enabling seamless decoupling between technology products and teams. This decoupling allows for greater agility and innovation, crucial for an industry as dynamic as finance.

In a conversation at the API Summit 2024, Deshpande emphasized the importance of APIs in facilitating compliance with regulatory requirements, a critical aspect of AI governance. For Goldman Sachs, operating in a highly regulated environment, maintaining transparency and security is non-negotiable. They leverage Kong’s comprehensive suite of tools to ensure their APIs meet rigorous standards for authentication, authorization, and data protection.

Deshpande noted, "Instead of every team implementing these principles from scratch, we can offer them through the platform. I think that’s a really killer feature." This centralized approach not only enhances compliance but also accelerates development, allowing teams to focus on building innovative solutions without being bogged down by infrastructure concerns.

Leveraging AI for Client-Centric Innovation

Goldman Sachs is exploring AI to innovate and enhance client experiences while ensuring these technologies are deployed safely and responsibly. Their use of APIs to abstract AI models provides a layer of control and flexibility, crucial for adapting to evolving business needs and regulatory landscapes. This strategic use of AI governance enables Goldman Sachs to experiment with AI in a controlled manner, aligning with their engineering tenets and business principles.

By integrating AI governance into their strategic initiatives, Goldman Sachs not only meets regulatory demands but also positions itself as a forward-thinking leader in the financial sector. This approach builds trust with clients and stakeholders, reinforcing their commitment to ethical and transparent AI practices.

This case study vividly illustrates how effective AI governance frameworks can empower organizations to leverage AI responsibly and innovatively, ensuring compliance while driving business growth and customer satisfaction.

For more insights into how AI governance can transform your organization, explore our AI Governance Playbook and learn how to balance innovation with security effectively.

Adopting effective AI governance is about creating frameworks that guide AI use in an ethical, transparent, and secure manner so that the technology can serve our best interests — not the other way around. The journey only just begins with setting clear policies and guidelines that govern how AI should be developed, deployed, and monitored so that everyone in the organization understands expectations and operates within ethical and legal boundaries.

Fostering a culture of responsibility is equally as important. Your teams should have a clear understanding of their specific roles and responsibilities. Additionally, investing in AI educational resources and training enhances AI literacy across the organization and empowers employees to make informed decisions and actively contribute to responsible AI initiatives.

By this point, it should be clear that transparency is the wheel that makes AI governance turn. Clear communication and processes build trust with consumers and stakeholders by helping them understand decision-making. This goes alongside embracing collaboration to bring the highest level of scrutiny and insights to AI governance. Organizations that provide cutting-edge knowledge and engagement opportunities see the greatest success in their AI governance strategies.

Kong helps organizations achieve AI governance through its comprehensive suite of tools designed to manage, secure, and monitor API-driven applications and services, which support the architecture of AI systems. With these tools, Kong empowers organizations to navigate the complexities of AI governance in one place and scale up effortlessly. 

• Secure and manage your APIs: Kong Gateway serves as a secure platform to connect to and manage LLMs, implementing authentication, authorization, and encryption protocols. Kong ensures that AI-driven APIs are accessed through a secure AI Gateway to comply with data protection regulations. 
• Centralized control: With Kong Konnect, organizations can use a centralized platform to manage all their APIs and services, including those powered by AI. This unified control plane simplifies the oversight of complex AI ecosystems, ensuring consistent enforcement of governance policies across all AI services. By providing comprehensive visibility into the entire API landscape, Kong Konnect facilitates monitoring compliance with established AI governance frameworks.
• Modern observability: Kong provides extensive logging and monitoring capabilities to ensure that all interactions with AI services are transparent and traceable. This level of AI observability is essential for AI governance, as it allows organizations to audit AI behaviors, identify and mitigate biases, and implement accountability in AI decision-making processes.
• Scalability and performance: Built to scale, Kong's infrastructure supports the growth of AI services without compromising governance standards. This scalability means that as AI services expand, they remain compliant, performant, and reliable to meet the dynamic needs of businesses while adhering to governance requirements.
• Plugin ecosystem for custom AI governance: Kong’s AI Gateway leverages Kong’s extensive plugin ecosystem to meet a variety of AI governance needs. Organizations can implement a variety of pre-built plugins that enforce additional governance checks, such as AI model validation, bias detection, or ethical compliance checks, directly within the API gateway.

Conclusion

AI governance plays a pivotal role in shaping the responsible, ethical use of AI in today's ever-evolving digital landscape. By prioritizing key pillars like data privacy, transparency, and accountability, organizations can both mitigate risks and scale AI effectively and safely. A robust AI governance framework sets you apart as a reliable, forward-thinking leader, showcasing your commitment to customers, regulators, and stakeholders alike. This proactive approach helps build lasting trust and positions your organization for success in an AI-driven future.

Want to learn more about how to adopt AI and multiple LLMs in a secure, governable Way? Check out the AI Governance Playbook to see how innovation and security can be better balanced for responsible AI adoption.

What is AI Governance?

AI governance is a framework comprising policies and best practices designed to help organizations deploy, manage, and optimize artificial intelligence in an ethical, transparent, and secure manner. It ensures that AI systems adhere to applicable laws and regulations while mitigating risks related to bias and data privacy.

Why is AI Governance Important?

AI governance is crucial for safeguarding ethical principles, ensuring regulatory compliance, and mitigating risks such as bias and data breaches. By setting clear standards for design, deployment, and oversight, organizations can build trust with customers, regulators, and stakeholders.

How Does AI Governance Reduce Bias in AI Systems?

AI governance reduces bias by implementing fairness and transparency measures throughout the AI lifecycle. This includes using diverse training data, conducting regular bias audits, and utilizing tools like LIME and SHAP to generate explanations for AI decisions, thereby identifying and correcting unfair outcomes.

Which Industries Benefit from AI Governance?

AI governance benefits nearly every industry that relies on or experiments with AI, including:

• Healthcare: Enhancing disease diagnosis and patient data management.

• Finance: Improving credit scoring and fraud detection.

• Retail: Enabling personalized shopping experiences and marketing.

These sectors maintain ethical and compliant AI practices through governance.

How Does AI Governance Address Data Privacy Concerns?

AI governance mandates strict data handling protocols, including encrypted storage, secure access management, and compliance with regulations like GDPR, CCPA, and HIPAA. These measures protect personal or sensitive data used by AI models from unauthorized access or misuse.

What is the Role of AI Governance in DevOps?

In DevOps, AI governance integrates ethical and compliance checks directly into development pipelines. Tools can automate bias detection and fairness tests within the continuous integration and delivery (CI/CD) process. Governance policies define how developers handle data, model versioning, and security controls.

How Does AI Governance Span the Entire AI Lifecycle?

AI governance applies to every stage of AI development and deployment, including:

• Data collection and management
• Model creation, testing, and validation
• Explainability and transparency practices
• Ongoing security and performance monitoring

Governance ensures the proper retirement or archiving of AI systems when they are no longer in use.

How Can Organizations Adopt AI Governance Effectively?

To effectively adopt AI governance, organizations should:

• Establish clear policies
• Train employees on AI ethics and data handling
• Prioritize transparency in AI operations
• Collaborate with public, private, and academic stakeholders to shape balanced, inclusive policies

Continuous monitoring and risk assessments are essential to adapt to new threats or regulations.

How Does Kong Help Organizations with AI Governance?

Kong provides a platform of tools—such as Kong Gateway, Kong Konnect, and Kong AI Gateway—to secure and manage API-driven applications powering AI systems. With features like centralized control, observability, and a plugin ecosystem for bias detection or ethical compliance checks, Kong ensures organizations can deploy AI responsibly and at scale.