From the thesis: "The Freedom of Information Act (FOIA), a cornerstone of U.S. democratic transparency, grants foreign adversaries the same access to federal agency information as U.S. citizens, creating national security risks. The proliferation of government data collection, open data initiatives, and artificial intelligence (AI) technologies has amplified this threat by enabling actors to aggregate and infer sensitive information through the mosaic theory. This thesis investigates how the U.S. Department of Homeland Security (DHS) can balance its transparency obligations under FOIA while protecting federal data assets from exploitation by technologically advanced actors. Leveraging a mixed policy and literature analysis, this thesis evaluates FOIA alongside related federal data policies, identifying gaps and opportunities for modernization. A modified organizational case study demonstrates how publicly released federal datasets can be aggregated to reconstruct a DHS database of sensitive information. Findings reveal that FOIA's statutory design leaves agencies vulnerable to the mosaic effect, as it fails to account for large-scale data aggregation risks recognized in other recent federal data policies. This thesis recommends that Congress modernize FOIA to align with current cybersecurity and data protection standards and that DHS prioritize the safeguarding of federal data assets as a national security priority until legislative reforms are enacted."