What is AI Governance? Tips and Best Practices

AI Governance is the framework, policies, and practices used to manage, monitor, and oversee AI systems to ensure proper use. AI, being a relatively new technology, introduces previously unexplored and distinct challenges. These include addressing bias, ensuring transparency, maintaining accountability, addressing accuracy issues, hallucinations, security, and more. AI governance ensures that AI operates ethically, safely, in alignment with societal norms, and provides correct information.

The convenience of GenAI introduces a complex web of AI risks that extend far beyond simple misuse. These risks are not theoretical; they are active threats that can lead to significant financial, reputational, and regulatory consequences. Understanding this new attack surface is the first step toward building an effective defense.

1. Bias and Unfairness AI systems can perpetuate or even exacerbate existing biases found in their training data, resulting in unfair outcomes. For example, algorithmic bias in model outputs can distort hiring algorithms, skew financial credit-scoring models, or introduce discriminatory criteria into customer onboarding workflows.
2. Privacy Violations - AI technologies can infringe on individual privacy. If the datasets the LLMs are trained on contain personal data, and this data is not stored or used properly, PII and other sensitive data might be unlawfully shared. 
3. Misuse - The innovative capabilities of AI provide vast potential for applications and services. These include harmful purposes, such as creating deepfakes, cyberattacks, phishing, or automating illegal activities.
4. Misinformation - AI can easily produce and spread false information. These could be due to hallucinations or intentionally malicious training. This can impact people’s knowledge, ideas and insights, influencing business processes and even disrupting democratic processes.
5. Ownership and Intellectual Property Rights - AI’s outputs can closely mimic existing content and creatives, raising the question of IP and ownership. There is also the question of whether it’s an IP violation to train LLMs on copyrighted information.
6. Accountability - Lack of transparency (“Black box”) and the fact that LLMs are not legal entities can make it difficult to determine liability when AI systems fail or cause harm. Recently, a court ruled that a Canadian airline was held liable for its misleading chatbot.
7. Security - AI systems are susceptible to attacks or misuse that can lead to exfiltration or corruption of data.
   

AI usage introduces a fundamentally different set of risks and behaviors that traditional IT governance frameworks were never designed to handle. Traditional IT governance frameworks were built for static applications and predictable workflows. AI introduces dynamic, user-driven interactions that require real-time visibility and enforcement beyond traditional controls.

Real-Time Risk Mitigation

Detect and prevent sensitive data leaks, unsafe AI prompts, or policy violations as they happen, rather than after the fact.

Secure, Responsible AI Adoption

Enable employees to leverage AI tools safely without restricting productivity, fostering innovation while minimizing organizational risk.

Enhanced Compliance and Audit Readiness

Maintain continuous oversight of AI usage across tools and users, making regulatory reporting and internal audits simpler and more accurate.

Making AI systems understandable and explainable to stakeholders, including users, developers, regulators, and the general public. 

Practical Implementation: Clear documentation of how AI algorithms work, what data they use, and how decisions are made.

The obligation of individuals, organizations, or governments to take responsibility for the outcomes of AI systems.

Practical Implementation: Defining who is accountable for AI-related decisions, actions, and consequences. Establishing mechanisms for holding stakeholders accountable, including legal frameworks, oversight bodies, and processes for addressing complaints or grievances arising from AI use.

Designing, deploying, and managing AI systems in alignment with ethical principles such as fairness, transparency, and accountability.

Practical Implementation: Adding guardrails to LLM development processes to review datasets and training results and ensure they support equitable outcomes for all individuals, regardless of demographic factors.

Detecting deviations from expected LLM behavior to mitigate risks such as biases or security threats, and ensure that systems operate in accordance with ethical standards and legal requirements.

Practical Implementation: Ongoing tracking of performance metrics, security vulnerabilities, ethical compliance, and regulatory adherence, as well as guardrails, as explained above. These should be implemented into feedback loops.

The people involved in defining ethical guidelines, regulatory frameworks, and best practices that govern AI technologies.

Practical Implementation: Inviting and involving developers, researchers, policymakers, regulators, industry representatives, affected communities, and the general public. Ensuring that diverse perspectives, concerns, and expertise are considered throughout the development, deployment, and usage of AI systems.

Safeguarding individuals’ rights to control their personal data and ensure its confidentiality and integrity throughout its lifecycle.

Practical Implementation: Enforce data anonymization, encryption, secure transmission, and compliance with global data protection frameworks, including the EU GDPR, the EU AI Act, Japan's APPI, and Brazil's LGPD.

The measures and practices implemented to protect AI systems from unauthorized access, malicious attacks, and data breaches, and to protect organizations from submitting sensitive data into AI systems.

Practical Implementation: Secure coding practices, encryption of sensitive data, regular vulnerability assessments and penetration testing, access controls and authentication mechanisms; monitoring for anomalous activities or potential threats; promptly responding to incidents; using an enterprise browser extension for GenAI DLP.

The capability of AI systems to provide understandable explanations for their decisions and actions.

Practical Implementation: Generating human-readable explanations, visualizing decision-making processes, and tracing back decisions to the input data and model features.

If you’re an organization looking to introduce, implement, or augment AI governance, follow these AI governance best practices:

• Ensure that data used for training and inference is anonymized.
• Conduct awareness programs to keep the workforce informed about potential risks and mitigation strategies.
• Create policies for typing and pasting data into AI applications. Akamai Workforce Protection can help enforce that only certain types of data or certain employees can access and/or use these applications, and in what ways.
• Restrict access to AI systems to authorized personnel only. When it comes to AI applications like ChatGPT, Akamai Workforce Protection’s access capabilities can help enforce these controls.
• Ensure datasets for training LLMs are diverse and comprehensive.
• Implement automated systems to monitor compliance with relevant regulations and standards.
• Monitor for toxicity and bias.

Akamai Workforce Protector’s AI DLP solution offers comprehensive protection for sensitive data when using AI applications like ChatGPT, Gemini, or Claude, without disrupting the user experience.

Akamai Workforce Protector allows defining specific data to protect, applying various data control methods (such as pop-up warnings or blocking actions), and enabling secure productivity without disrupting the user experience.

This solution allows organizations to utilize AI’s capabilities while preventing accidental data exposure, with customizable controls for different user needs and security levels.

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