An AI governance platform ensures that AI systems are developed responsibly and transparently. “It helps mitigate risks, such as data privacy breaches, model inaccuracies, and drift, and build trust with stakeholders,” says Jen Clark, director of advisory/technical enablement services at business consulting firm Eisner Advisory Group, in an email interview.
AI governance should extend an enterprise’s overall data governance commitment by reducing AI bias and increasing transparency, says Dorotea Baljevic, principal consultant, manufacturing and digital engineering, with technology research and advisory firm ISG. “AI governance covers much more than the AI system itself to include the necessary roles, processes, and operating models needed to enact AI,” she notes in an online interview.
AI automates and speeds decision-making. Yet there remains a need to create some type of audit trail that shows the decisions being made and allows decision reversals, if necessary, says Kyle Jones, senior manager of solutions architecture at AWS, in an email interview. “A reliable AI governance platform needs to meet the needs of the business today and can be updated and changed as time goes on so that results continue to meet business needs.”
Platform Attributes
AI governance platforms are similar to their counterparts in engineering operations, and cybersecurity best practices, including continuous monitoring, alerting, and automated escalations, all supported by a robust incident management process, Clark says. “What sets AI governance apart is the integration of automation to manage the models themselves, often referred to as machine learning ops or MLOps.” This includes automation to validate, deploy, monitor, and maintain models.
An effective AI governance platform includes four fundamental components: data governance, technical controls, ethical guidelines and reporting mechanisms, says Beena Ammanath, executive director of the Global Deloitte AI Institute. “Data governance is necessary for ensuring that data within an organization is accurate, consistent, secure and used responsibly,” she explains in an online interview.
Technical controls are essential for tasks such as testing and validating GenAI models to ensure their performance and reliability, Ammanath says. “Ethical and responsible AI use guidelines are critical, covering aspects such as bias, fairness, and accountability to promote trust across the organization and with key stakeholders.” Additionally, reporting controls should be put in place to support thorough documentation and the transparent disclosure of GenAI systems.
Team Building
There’s no one-size-fits-all framework for AI governance. “Rather than applying universal standards, organizations should focus on developing AI governance strategies that align with their industry, scale, and goals,” Ammanath advises. “Each enterprise and each industry has unique objectives, risk tolerances, and operational complexities, making it essential to build a governance model tailored to fit specific needs, leveraging context aware approaches.”
“AI governance requires a multi-disciplinary or interdisciplinary approach and may involve non-traditional partners such as data science and AI teams, technology teams for the infrastructure, business teams who will use the system or data, governance and risk and compliance teams — even researchers and customers,” Baljevic says.
Clark advises working across stakeholder groups. “Technology and business leaders, as well as practitioners — from ML engineers to IT to functional leads — should be included in the overall plan, especially for high-risk use case deployments,” she says. “From there, it’s easier to divide and tackle the plan, either by building custom workflows within your cloud provider’s ML/AI toolkit or by purchasing a solution and integrating it into an existing governance program.”
Avoiding Mistakes
The biggest mistake when implementing AI governance is treating it as a static, one-time implementation instead of an ongoing, adaptive process, Ammanath says. “AI technologies, regulations, and societal expectations evolve rapidly, and failing to design a flexible, scalable framework can result in outdated practices, increased risks, and loss of trust.” Additionally, failing to implement comprehensive controls and to continuously adapt to evolving marketplace threats can result in significant vulnerabilities that undermine the security and integrity of AI operations.
The biggest mistake enterprises make is focusing on specific models rather than workflows. “Models are constantly changing and improving,” Jones notes. “There’s not, and will never be, a single ‘best’ model.” Instead, he advises enterprises to focus on workflows that can be effectively automated.
Parting Thoughts
This is an exciting time in technology, with the potential to fundamentally change everything enterprises are doing, Jones says. “IT people should focus on business problems that can be automated, starting small and scaling out,” he advises. Use existing IT knowledge in areas such as abstraction, microservices, and loose coupling, all of which AI can amplify. “Start with projects that deliver business value to earn the right to move forward into more IT-centric improvements that reduce overall costs.”