GenAI governance covers all the policies, practices, and frameworks used to monitor GenAI systems to ensure their integrity and security. This theoretical concept is of great importance, since it can prevent business embarrassments, legal issues, and ethical injustices. For example, popular design tool Figma recently pulled back its use of GenAI because it plagiarized Apple’s design. GenAI governance could have prevented this.
In this blog post, we explain what GenAI governance is, why it’s needed, and most importantly – how to implement it. Read on to ensure your business’s use of GenAI aligns with the required standards, to meet your business goals.
What is Generative AI Governance?
GenAI Governance is the frameworks, policies, and practices used to manage, monitor, and oversee generative AI systems to ensure proper use. Generative AI is a new technology, therefore introducing previously unexplored and distinct challenges. These include addressing bias, ensuring transparency, maintaining accountability, accuracy issues (aka “hallucinations”), security, and more. GenAI governance ensures that generative AI operates ethically, safely, in alignment with societal norms, and provides correct information. This is the GenAI governance definition.
Challenges of Generative AI Governance
Why do organizations need to consider GenAI risks? Some of the top GenAI Challenges include:
- Bias and Unfairness – GenAI systems can perpetuate or even exacerbate existing biases found in their training data, resulting in unfair outcomes. For example, skewed answers can lead to not recruiting women, biased law enforcement toward minorities, and better loan approval terms to privileged groups.
- Privacy Violations – GenAI 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.
- Misuse – The innovative capabilities of GenAI provide vast potential for applications and services. These include harmful purposes, such as creating deepfakes, cyberattacks, phishing, or automating illegal activities.
- Misinformation – Generative AI can easily produce and spread false information. These could be due to hallucinations or intentful malicious training. This can impact people’s knowledge, ideas and insights, influencing business processes and even disruptnig democratic processes.
- Ownership and Intellectual Property Rights – GenAI’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.
- 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.
- Security – AI systems are susceptible to attacks or misuse that can lead to exfiltration or corruption of data.
Key Foundations of Generative AI Governance
Generative AI governance is made up of processes, tools and frameworks. When building your plan, consider the following AI governance factors:
- Transparency – 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.
- Accountability – 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.
- Ethical Usage – 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.
- Continuous Monitoring – 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.
- Stakeholder Involvement – 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.
- Privacy – Safeguarding individuals’ rights to control their personal data and ensure its confidentiality and integrity throughout its lifecycle.
Practical Implementation: Data anonymization, encryption, secure storage and transmission, and adherence to data protection regulations such as GDPR or CCPA.
- Security – 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.
- Explainability – 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.
Best Practices for Governing Generative AI: Ensuring Compliance, Privacy, and Security
If you’re an organization looking to introduce, implement, or augment GenAI governance, follow these GenAI governance best practices:
- Restrict access to AI systems to authorized personnel only. When it comes to SaaS GenAI applications like ChatGPT, LayerX’s access capabilities can help enforce these controls.
- Create policies for typing and pasting data into GenAI applications. LayerX can help enforce that only certain types of data or certain employees can access and/or use these applications, and in what ways.
- Ensure datasets for training LLMs are diverse and comprehensive.
- Ensure that data used for training and inference is anonymized.
- Implement guardrails throughout model training and deployment to check for governance issues.
- Monitor for toxicity and bias.
- Implement automated systems to monitor compliance with relevant regulations and standards.
- Conduct awareness programs to keep the workforce informed about potential risks and mitigation strategies.
- Establish a robust incident response plan to address potential security breaches or compliance violations.
Secure Your Use of GenAI with GenAI DLP
LayerX’s GenAI DLP solution offers comprehensive protection for sensitive data when using Generative AI applications like ChatGPT, Gemini, or Claude, without disrupting the user experience.
LayerX 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 GenAI’s capabilities while preventing accidental data exposure, with customizable controls for different user needs and security levels.
- Disable or limit GenAI browser extensions
- Control pasting and typing of sensitive data within applications
- Monitor use
