From Black Box to Glass Box: How Explainability in AI Supports Regulatory Readiness

AI adoption across compliance, privacy, and security operations is no longer aspirational; it’s operational. From triaging privacy incidents to automating breach risk assessments, organizations are leveraging AI systems and algorithmic decision-making to scale governance and streamline regulatory risk management.

But with that acceleration comes a new class of enterprise risk—one rooted in AI opacity.

AI systems that behave like “black boxes”—generating outputs without clear logic, lineage, or accountability—may offer short-term efficiency but can severely compromise trust and defensibility. In regulated industries, opacity isn’t just problematic; it’s potentially non-compliant and increasingly out of step with evolving expectations for AI risk management and algorithmic accountability.

Today’s global regulatory environment is converging around a foundational principle: you can’t govern what you can’t explain. From Brussels to Washington, oversight bodies are moving swiftly to mandate transparency, traceability, and responsible design in AI systems. The shift from black box to glass box isn’t just technical—it’s strategic. It reflects the need for AI governance practices that are auditable, ethical, and aligned with both regulatory frameworks and public expectations.

What Is Explainability in AI and Why Does It Matter for Compliance?

Explainability in AI refers to the ability to articulate how and why an AI system arrives at a specific output or recommendation. While closely related to interpretability—the degree to which humans can understand a model’s internal mechanics—explainability is about making decisions transparent to non-technical stakeholders: auditors, regulators, legal teams, and individuals impacted by AI-driven outcomes.

This is no longer a theoretical concern: it’s now a core requirement for compliance and AI risk assessment.

Consider key regulatory milestones:

• GDPR’s Article 22 protects individuals from solely automated decisions with legal or similarly significant effects and may imply a “right to explanation.”
  
• The EU AI Act classifies certain AI systems, such as hiring, credit scoring, and law enforcement, as high-risk and requires extensive documentation, transparency, and risk governance mechanisms.
  

In the U.S., the AI Executive Order (2023) mandates that federal agencies adopt responsible AI principles, with an emphasis on explainability, fairness, and oversight.

In high-stakes environments, explainability is a prerequisite for auditability, regulatory defensibility, and organizational resilience. It’s not just about understanding AI—it’s about proving that your AI can be trusted under scrutiny.

The Regulatory Risks of Opaque AI

AI systems that lack transparency introduce blind spots, which can evolve into full-blown crises in the context of compliance. Opaque AI undermines internal accountability and limits an organization’s ability to respond effectively to audits, litigation, or stakeholder concerns.

The consequences are increasingly visible:

• Algorithmic bias in credit, hiring, or resource allocation decisions has triggered lawsuits and regulatory investigations.
  
• Insufficient documentation around AI decision logic has stalled incident investigations and hampered internal audit functions.
  
• Privacy incidents triaged by black-box models have led to compliance breakdowns, undermining both risk governance and public trust.
  

Ultimately, if your organization can’t explain how an AI model made a decision, you may not be able to defend it. In the eyes of regulators and courts, that’s a serious—and increasingly unacceptable—risk. Lack of explainability is now a critical exposure point in enterprise AI risk assessments.

What Glass-Box AI Looks Like in Practice

A “glass box” approach doesn’t abandon automation; it strengthens it through traceability, transparency, and human-centric design. These systems are intentionally built to make AI-driven decisions clear, interpretable, and accountable—not just to data scientists but to the stakeholders responsible for privacy, security, and regulatory compliance.

Key characteristics of glass-box AI include detailed model traceability, visibility into training data lineage, and human-in-the-loop mechanisms that allow experts to validate or override decisions. Instead of outputs that appear arbitrary or opaque, glass-box systems provide clear decision paths, explainable logic, and context for downstream risk decision-making.

Embedding Explainability into Compliance Workflows

For CISOs, CIOs, and risk executives, embedding explainability into operational workflows is essential for building an effective AI risk management program. That starts with intentional procurement and implementation practices. When evaluating AI-driven tools for compliance, privacy, or risk functions, leaders should ask:

• Can we trace how the AI reached this decision?
  
• Does the platform support human oversight and override mechanisms?
  
• Is there an audit trail aligned with our regulatory obligations?
  
• How well does the tool support ongoing AI risk assessment?

Beyond technology selection, risk governance must be clearly defined and implemented. It is critical to assign responsibility for AI oversight, whether through a centralized function or a cross-functional committee. Documentation processes should be built into the full AI lifecycle, capturing everything from training data inputs to model versioning and decision outcomes.

Adopting standards like the NIST AI Risk Management Framework or ISO/IEC 42001 can help formalize these efforts, offering a consistent foundation for scalable, explainable, and compliant AI practices.

The Road Ahead: Trustworthy AI as a Competitive Advantage

As regulations evolve, explainable AI is quickly becoming a strategic asset, not just a compliance obligation. Organizations that can demonstrate robust, transparent AI governance will be better positioned to meet expanding legal requirements, build customer confidence, and withstand scrutiny in moments of crisis.

Trust is the throughline. In industries such as financial services, healthcare, or critical infrastructure, trust in automation is fundamental. Regulators, customers, and partners all expect systems to be fair, accountable, and understandable. Explainability makes that possible.

By investing now in transparent AI systems and proactive AI risk management, organizations aren’t just preparing for audits—they’re future-proofing their operations and strengthening stakeholder relationships. In a world of accelerating regulation and intensifying risk, moving from black box to glass box is not only smart—it’s strategic.

Interested in operationalizing explainable AI in your risk and compliance workflows? Discover how RadarFirst facilitates transparent AI governance in accordance with the EU AI Act and beyond.