Modernizing Border Security: Unlocking Data Value for a Next-Generation Mission

Garth White, Director, Intelligence and Homeland Security, Oracle 

Decades of senior technology and data leadership at the Department of Homeland Security (DHS) have revealed a powerful truth: the success of every mission rest on data - how securely it is protected, how rigorously it is governed, and how quickly it can be transformed into actionable intelligence. We must leverage AI to accelerate our response to today’s dynamic threat landscape, and ensure AI enabled data becomes the backbone and lifeblood of border security operations, informing every decision and determining operational outcomes. The approach to achieving border intelligence goals has fundamentally evolved. Rather than collecting more data or building larger data repositories, our focus should dwell on unlocking data’s value where it resides, in real time and with accountability. Effective insight must reach officers at the point of decision within seconds, without compromising privacy, civil liberties, or public trust.

Changing Mission Conditions Require Adoption of Accountable AI

Modern mission requirements dictate analyzing data in place and deploying governed analytics and artificial intelligence (AI) at the edge. This enables officers to obtain a complete, risk-informed picture now of encounter, while policy, identity, and audit measures remain centralized.

AI must be governed as a system of record, not merely as a laboratory experiment. This includes maintaining a dynamic catalog of approved models, documenting training data, ownership, and mission decisions. Models trained on sensitive border data must comply with the same access controls, auditing, and retention policies as the underlying data.

For critical actions such as denials, interdictions, or enforcement, AI is designed to augment - not replace - highly trained officers. Guidelines should clearly define when and how human intervention is required. Routine red teaming, monitoring, and continuous testing for bias and accuracy are crucial to preventing minor issues from escalating into significant failures.

Public Transparency and Accountability

Technical safeguards are essential but insufficient by themselves. Border systems directly impact individual rights and freedoms, making it imperative to embed privacy and civil liberties considerations from the outset. This includes data minimization, purpose limitations, role-appropriate access, and strict controls on secondary data use.

Accountability to the public requires transparency and explainability. When AI decisions affect an individual's ability to travel, migrate, or work, officers need to clearly articulate the data used, decision-making processes, and paths available to correct errors. AI built in to border systems must deliver truth and neutrality. Decision processes must be traceable and auditable, empowering oversight and leadership to fully reconstruct actions taken. Ultimately, efficiency without trust jeopardizes long-term mission success.

Enabling Secure Cross-Agency Collaboration

• Successful, real-time data sharing across DHS components depends on aligned incentives and frictionless collaboration. Best practices include:
• Establishing shared operational outcomes that enhance mission metrics and collaboration value.
• Building a governed data layer with common standards and attribute-based access, ensuring stakeholders view the appropriate information at the correct level.
• Formalizing trust through memoranda of understanding (MOUs), data-sharing agreements, and joint governance structures that give data owners meaningful oversight.
• Streamlining secure sharing processes so that compliance is not a barrier but the path of least resistance, reducing the temptation for insecure local workarounds.

Building a Next-Generation Border Security Ecosystem

Delivering a next-generation border ecosystem requires investing in both mission-aligned workforce skills and robust measurement frameworks. Moving beyond merely counting unauthorized crossings, future success should be assessed by:

• Reducing time-to-insight at the edge – using AI and other technologies to enable officers to access complete, risk-based intelligence within seconds.
• Improving interdiction quality by enhancing hit rates, reducing false positives, and expediting the clearance of lawful travelers and shipments.
• Strengthening resilience and surge capacity, ensuring consistent operations during migration surges, cyber incidents, or large-scale events.

A Next-Generation Workforce includes:

• Data and AI practitioners grounded in the operational realities of ports, field environments, and command centers.
• Cloud-native and DevSecOps engineers who integrate security into automated pipelines while producing compliant, resilient architectures.
• Product and platform leaders who treat data and AI services as user-centered products with clear outcomes.
• Translators and change agents who facilitate effective collaboration between operators and technologists.

If leaders get the foundations right — data quality, identity, governance and trust — modern cloud and AI can help build a more efficient and resilient border system.