Essential AI Governance Documents to Build Trust in AI

Risk management documents are foundational to good risk management. They are also crucial for managing AI risks as they establish structured processes to identify, assess, mitigate, and monitor potential threats throughout the AI system’s lifecycle.

Risk framework documents support AI governance by formalising the processes needed to mitigate risks, align with organisational goals, and ensure that AI systems operate safely and ethically. Alignment to standards such as ISO 31000 is good practice.  Conformance to the NIST-AI-600-1 Risk Management Framework is essential as this guideline specifically addresses AI-related risks like biases, security vulnerabilities, and transparency.

The risk management framework typically outlines the processes for evaluating risks, ensuring that AI systems are developed, deployed, and monitored within established boundaries i.e. risk appetite and tolerance. This creates a solid foundation for AI governance by ensuring transparency and accountability.

By clarifying how much risk the organisation is willing to accept, it ensures that AI capabilities align with strategic goals without exceeding acceptable limits.

Importantly, the risk management framework includes provisions for continuous monitoring of risks, including AI performance, detecting potential risks such as model drift, security vulnerabilities, or changes in data quality. This proactive approach helps reduce AI risks over time.

2. AI Ethical Guidelines

Ethics guidelines, which may vary by country, industry, or organisation, outline standards for fair, transparent, and accountable AI usage. These guidelines address concerns such as bias, privacy, discrimination, and decision-making transparency. These guidelines provide a moral and ethical framework for AI development, helping organisations avoid unethical practices such as biased algorithms or opaque decision-making. By adhering to these guidelines, organisations foster trust among stakeholders, including users, regulators, and the public.

AI ethics guidelines are often spread across multiple documents within an organisation, as different aspects of ethics intersect with legal, operational, technical, and governance requirements.  Examples include:

• AI Ethics Charter or Social Impact Guidelines
• Data Governance Policy
• Bias and Fairness Assessment Framework
• Cybersecurity Policies
• Risk Management Policies
• AI Incident Response Plan/ Playbook

3. Information Technology Governance 

Good AI governance sits within the organisations current IT governance framework.

Compliance with ISO/IEC 38500 – Governance of IT ensures robust oversight of AI deployment within the organisation. It emphasises that AI systems are deployed effectively, transparently, and with clear accountability, minimising associated risks. Strong governance under this standard also boosts stakeholder confidence by demonstrating the organisation’s commitment to responsible management and control of AI systems.

Algorithmic Impact Assessments (AIA) and Data Protection Impact Assessments (DPIA) evaluate the potential social, legal, and ethical impacts of AI systems. AIAs focus on the broader societal impacts, while DPIAs are specifically concerned with privacy and data protection.

Responsibility matrices assign clear roles and responsibilities to individuals or teams involved in the AI lifecycle (development, deployment, monitoring). These documents clarify who is accountable for what, ensuring that all aspects of the AI system are properly managed. For AI governance, a RACI matrix (Responsible, Accountable, Consulted, Informed) is a popular framework to ensure that everyone involved in the AI lifecycle understands their role.

4. Stakeholder Engagement

Stakeholder analysis documentation identifies key internal and external stakeholders who interact with or are affected by the AI system. This includes employees, customers, regulators, and third parties.

Stakeholder communication plans detail how information about the AI system’s development, risks, and performance will be shared with stakeholders, including users, customers, employees, and regulators

5. AI System Design Specifications

Collectively, AI system design documents play a crucial role in fostering transparency, accountability, and continuous improvement in AI projects. They ensure that the technical foundations of AI systems are well understood, validated, and monitored, which is essential for ethical and effective AI deployment.

Technical system design specifications provide a comprehensive overview of the AI model’s architecture, including details on the algorithms employed, the data processing pipeline, and the overall system design. A well-documented technical design serves as a blueprint for developing and implementing AI systems. It ensures that all stakeholders, including developers, data scientists, and project managers, have a clear understanding of how the system is constructed.

Model training and validation reports are critical for ensuring the integrity and reliability of AI systems. These reports detail the training process of the AI model, including the datasets used, pre-processing steps, training algorithms, validation methods, and the results of various testing phases. They document how the model was developed, the rationale behind the choices made during training, and any iterations or adjustments performed to improve model performance.

6. AI Operational Policies

Operational policies ensure that AI systems remain functional, understandable, and manageable throughout their lifecycle. They strengthen governance by providing the necessary controls, training, and documentation to maintain the AI’s reliability, transparency, and user competence.

AI system maintenance and update policies and guidelines outline the procedures and guidelines for continuously monitoring, maintaining, and updating AI systems to ensure their performance remains optimal over time. This includes routine system checks, retraining of AI models with new or updated data, patching for security vulnerabilities, and addressing algorithmic drift.

Model explainability and transparency documentation provide detailed explanations of how the AI model operates, including how decisions are made, what data influences the outputs, and any factors that could affect the system’s decisions. They often include the use of explainability tools like LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations), which help interpret model predictions.

User training documents provide guidelines and training materials to internal users, such as employees, operators, or administrators, on how to interact with and operate AI systems. This training might include understanding how to input data, how to interpret AI-generated outputs, how to flag issues or anomalies, and how to ensure that human oversight is maintained.

7. Data Management 

Data management documents play a crucial role in establishing a solid foundation for responsible AI development. By outlining procedures for sourcing, quality assurance, and privacy compliance, these documents empower organisations to harness high-quality data effectively.

Data sourcing policies outline the origins of the data used for training and operating AI systems. These policies specify whether the data is sourced internally from company databases, externally from public datasets, or from third-party providers. They may include criteria for selecting data sources, such as relevance, reliability, and legal compliance.

Data quality and governance guidelines establish standards for ensuring that data used in AI systems is accurate, complete, consistent, and representative of the intended population. This includes defining processes for data validation, cleaning, and monitoring to identify and rectify issues such as duplicates, inaccuracies, or biases.

Data privacy policies detail how organisations handle and protect personally identifiable information (PII) in compliance with regulations such as the General Data Protection Regulation (GDPR) and/or other relevant data protection laws. These policies outline the procedures for collecting, storing, processing, and sharing PII, as well as the rights of individuals regarding their data.

8. Cybersecurity 

AI systems are exposed to adversarial attacks, data breaches, and other vulnerabilities.

Cyber Security Documents are essential components of a comprehensive framework designed to safeguard AI systems from various threats and vulnerabilities.

Cybersecurity policies outline the organisation’s approach to securing its AI systems against attacks, adversarial inputs, and data breaches. They establish guidelines for identifying and mitigating potential risks, including the implementation of security measures such as firewalls, encryption, and access controls. Additionally, these policies emphasise the importance of regular security audits and the need for continuous monitoring to detect and respond to potential threats proactively. By setting clear expectations for cybersecurity practices, organisations can create a culture of security awareness among employees, reducing the likelihood of human errors that could lead to security breaches.

Vulnerability assessment and penetration testing are crucial for identifying potential security gaps within the AI system.

Vulnerability assessments systematically analyse the AI system for weaknesses that could be exploited by attackers, while penetration testing simulates real-world attack scenarios to evaluate the system’s defences. The findings from these reports help organisations prioritise security improvements and allocate resources effectively. By addressing identified vulnerabilities, organisations can strengthen their AI systems against adversarial attacks.

Preparedness is key to managing incidents involving AI systems. Incident response plans and playbooks outline the steps to take in the event of a security breach, system failure, or regulatory violation. These plans include defined roles and responsibilities for team members, procedures for containing and mitigating the incident, and guidelines for communicating with stakeholders, including affected users and regulatory bodies. By having a structured response protocol in place, organisations can minimise the impact of an incident, ensure a swift recovery, and maintain trust with stakeholders.

Regularly testing, reviewing and updating these plans ensures they remain effective and relevant as the threat landscape evolves.

Incident and breach reports document any incidents related to AI systems, including data breaches or algorithmic failures.  They provide a detailed analysis of the causes, impacts, and corrective actions taken in the event of an incident.