Solving AI Governance Challenges: Ensuring Compliance and Control

Strong governance has the potential to drive strategic AI innovation. Organizations that embrace generative AI are not inherently at odds with rising thresholds for control, transparency, accountability and compliance. With solid AI governance, enterprises can meet these growing expectations without slowing momentum.

AI governance includes policies, processes and tools designed to ensure that systems align with legal requirements, reflect ethical principles and support operational goals. There are challenges to establishing AI governance, especially at an enterprise level. Different countries are issuing and updating regulations quickly. Unapproved or “shadow” AI tools are increasingly prevalent. Various internal divisions may have different adoption processes for new technology. Regardless of the source of the friction, operating without clear AI governance risks disruptions that could slow operations and erode trust. 

Public AI environments magnify these risks because of potential data exposure and lack of model control. By bringing AI workloads into private AI environments, businesses can better protect sensitive data and adapt their systems to meet the requirements of specific industries or regions.

When AI infrastructure is designed with privacy, compliance and oversight in mind, generative tools become less of a liability and more of a strategic asset. Read on, or jump to Forrester’s AI Predictions for 2025, to unpack the possibilities.

Turn regulations into a competitive edge

Today’s enterprises face a quickly evolving regulatory landscape that is increasingly fragmented. In the European Union, the AI Act is introducing risk-tiered obligations for everything from transparency to data governance. In the United States, executive orders are pushing for agency-led accountability and sector-specific guidance. Across Asia-Pacific, jurisdictions like Singapore and Japan are advancing voluntary frameworks focused on trustworthy innovation. Meanwhile, requirements related to data sovereignty are also intensifying. Countries such as India, Brazil and South Korea have introduced or strengthened rules that limit how and where sensitive data can be stored, processed or transferred — especially when AI is involved.

Even when multiple laws aim for widespread goals like protecting user privacy, the specifics vary. One jurisdiction might prioritize consent protocols, while another emphasizes data retention policies or explainability standards. In addition, legal interpretations can shift mid-project, further complicating matters.

To keep pace, enterprises must build and maintain systems that support explainability, accountability and transparency across a variety of contexts. That includes being able to track how AI models use inputs, generate outputs and evolve over time. It also means documenting how data is collected, processed and retained. With that level of visibility in place, teams can respond to audit inquiries or legal shifts without reengineering core infrastructure.

Many enterprises are investing in adaptability-focused governance frameworks, seeing them as drivers of resilience and rigor alike. Such approaches continually standardize core practices across regions and use cases, including access controls, policy enforcement and audit documentation. Enterprise-grade platforms like SUSE AI incorporate ISO 27001/27701, FIPS 140-3, Common Criteria EAL-4+ and other certifications directly into platform architecture. As a result, they reliably and efficiently support organizations that must align with numerous legal expectations. These solutions feature auditable safeguards like role-based access controls and policy enforcement tools, which further reduce uncertainty and improve readiness.

Security by design

AI adoption introduces new pressures on data protection. Sensitive information can be exposed through targeted attacks, misconfigured tools or even well-intentioned exploration of emerging technologies. Today’s employees regularly test out generative AI tools without the oversight of an organization’s IT department, occasionally incorporating them into workflows. Known as shadow AI, these unmanaged deployments typically bypass internal policies, undermining oversight and increasing risk of data exposure. A governance model that supports centralized visibility and policy enforcement can mitigate these dangers while still honoring the value of experimentation.

In private AI environments, the strength of your governance starts with the way that your systems are built. A design-first approach embeds security and privacy protections into the infrastructure from the start. This upfront integration creates a strong baseline that supports more consistent control and visibility across AI workloads. Teams can encrypt sensitive data during transit and at rest, isolate workloads to reduce exposure, and implement observability features that flag unusual system behavior before it escalates. SUSE AI includes zero trust security principles and advanced observability features, helping organizations to detect anomalies early and to consistently enforce security policies across environments.

Many AI solutions rely on third-party components and open source tools, making supply chain integrity a critical concern. Enterprises need visibility into where code originates, how it’s validated and when it’s updated. Architectures that support reproducible builds, cryptographic signing and structured update protocols can help achieve and maintain the necessary transparency.

SUSE AI is designed with these principles in mind. The platform supports confidential computing, secure software supply chains and deployment flexibility — including fully air-gapped environments for high-security use cases. The popular open source AI tools and components in its library are built using the common criteria-certified SUSE secure supply chain, offering an additional layer of assurance for enterprises with strict governance requirements.

Freedom to adapt, confidence to scale

Achieving AI sovereignty means more than selecting where your workloads run. It requires the ability to control models, manage data and define the policies that shape system behavior. In private environments, that control remains with the enterprise rather than with a third-party platform. This means that organizations can tailor deployments to their operational, regulatory and strategic needs without compromising visibility or autonomy.

SUSE AI is purpose-built for generative AI and designed to support this level of flexibility. As a cloud native deploy and runtime platform, it enables enterprises to run the large language models of their choice across cloud, on-premises, hybrid and fully disconnected environments. This deployment flexibility ensures that governance standards are upheld regardless of infrastructure.

Consistency is key to scaling safely. With SUSE AI, observability, policy enforcement and other governance tools remain stable across environments. As a result, organizations are better equipped to preserve oversight as their systems grow more complex. Portability, especially when grounded in open source principles, reinforces this consistency. It offers technical interoperability as well as long-term strategic resilience. Enterprises that avoid vendor lock-in can better adapt, reconfigure or move away from specific architectures as needed and on their own terms.

Keep people in control of the machine

AI is most valuable as an enhancement to human judgment, rather than a replacement. That principle is especially important in high-stakes workflows where decisions may affect customers, patients or the broader public. 

Human-in-the-loop configurations keep people directly involved in reviewing, validating or adjusting AI-generated outputs. This involvement helps maintain quality and accountability. When an AI ethics framework is underpinned with human-in-the-loop setups, oversight activities become an organic extension of broader organizational values.

AI explainability is another key component of human-first AI approaches. Tools like model documentation, decision-path visualization and data lineage tracking can help teams understand the influences behind outputs. In complex environments, this level of transparency ensures that AI systems operate in line with internal policies and external expectations.

Faster paths to responsible deployment

In private AI environments, the transition from experimentation to organization-wide deployment often involves roadblocks. Unclear compliance requirements, unexpected security concerns and last-minute retrofits, among other challenges, may slow down deployment and increase the cost of scaling. 

When governance and security are fully embedded in process foundations, teams are better able to move quickly and responsibly. Integrated observability, portability and modular deployment options all play a part in reducing handoff delays between teams. An enterprise-grade AI platform can help operationalize these key capabilities and support cross-functional implementation efforts.

Future-proof governance starts now

Responsible AI practices and strong AI governance strategies do more than mitigate risk. They prepare organizations to navigate change with confidence. As global standards like the NIST AI Risk Management Framework and ISO/IEC 42001 continue to evolve, enterprises need governance strategies that can flex with them. Businesses working across borders face even greater complexity given overlapping legal and policy frameworks. 

Embedding compliance, security and adaptability into the core of private AI infrastructure can enable innovation and productivity without sacrificing oversight. A consistent and comprehensive AI governance framework simplifies stakeholder reviews, speeds up approval cycles and lowers the burden on internal teams. With the right approach, progress doesn’t have to come at the cost of control.

See what Forrester predicts for AI in 2025 — and what it means for enterprises.