Shift-Left Governance Brings Data Controls Upstream for AI
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Shift-Left Governance Brings Data Controls Upstream for AI
The worst time to discover an AI data governance gap is after a system reaches production and turns a hidden data problem into a critical business risk.
By executing tasks and making decisions with less -- or no -- human intervention, agentic AI can improve operational efficiency, but it also raises new questions about data quality, access and accountability. Those considerations, along with AI's transition from experimentation to production, are pushing enterprises to adopt shift-left governance, also known as upstream governance.
Putting it into practice requires organizations to revise their governance oversight structure, implement it at the start of the application development process and deploy new technologies that automate repetitive governance tasks, according to industry executives.
Why AI governance needs to start earlier in the process
Enterprises have become more receptive to shift-left governance as AI initiatives have exposed the shortcomings in existing data management and governance practices.
According to Balazs Fejes, president and CEO of EPAM Systems, most organizations are still building a foundational framework for data governance that lacks the processes required to handle the more complicated governance issues AI introduces.
"They start facing the reality that they don't have the basics right [and] they need to put together something much more robust, much more programmatic," Fejes said.
Shift-left governance breaks from centralized, review-based governance, although organizations pursuing a data mesh architecture might already use a similar approach.
"One of the core principles of data mesh is federated computational governance," Fejes said.
Under that model, domain representatives agree on governance rules that are implemented in the organization's data platform for automated enforcement.
Fejes said this model replaces committee-based governance with an approach with governance controls that travel with the data, such as ownership information, quality metrics and contracts.
Governance rules and ownership are attached to data assets and AI models when they are created, placing accountability on the teams responsible for them throughout their lifecycles, Fejes added.
Shift-left governance depends on a data architecture that enables policies and controls to be embedded in data products and AI workflows. Fejes said the key parts of this foundation are the data platform, data products, data contracts, ontologies and semantic layers. Semantic models are especially important in providing the business context that helps agents interpret enterprise data consistently, he said.
Steps to Adopting Shift-Left Governance:
- Start with an inventory of AI risks
- Revise the governance oversight structure
- Develop the foundation for embedded governance
- Use automation for enforcement
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