The most effective and proactive strategy to mitigate the risk of deepfake attacks is to validate the provenance of the data source. Data provenance involves establishing and verifying the origin, history, and chain of custody of a digital asset (e.g., video, audio, image). By using techniques like cryptographic signatures, digital watermarking, or blockchain-based ledgers, an organization can confirm that the media has not been tampered with and originates from a trusted source. This approach addresses the core issue of authenticity, making it a more fundamental and reliable control than reactive detection methods or fallible human judgment.

A. Relying on human judgment for oversight: Humans are notoriously unreliable at detecting sophisticated deepfakes, as these are specifically designed to deceive human perception. This makes human judgment a weak primary control.

B. Limiting employee access to AI tools: This is an internal control that does not prevent externally generated deepfake attacks, which are a common threat vector (e.g., a phishing email with a deepfaked video).

D. Using a general-purpose large language model (LLM) to detect fraud: General-purpose LLMs are designed for text-based tasks and are not the appropriate tool for analyzing the complex visual or audio artifacts characteristic of deepfakes.

1. ISACA

Artificial Intelligence Audit Toolkit

2023. The toolkit emphasizes the importance of data integrity and governance as foundational controls for AI systems. In the section on AI-specific risks

it discusses the need for controls that ensure the authenticity and integrity of data inputs

which directly relates to validating provenance to counter threats like deepfakes. (Reference to Data Integrity and Input Validation controls).

2. Guera

D.

& Delp

E. J. (2018). "Deepfake Video Detection Using Recurrent Neural Networks." 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). While focusing on detection

the paper's context highlights the challenges and the "arms race" between generation and detection

implicitly supporting the need for more fundamental solutions like provenance. The limitations of detection underscore the superiority of proactive authentication. (DOI: 10.1109/AVSS.2018.8639163).

3. MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)

"Detecting Photoshopped and Fake Images." Research from MIT and other institutions consistently points to the difficulty of detection and the value of source verification. Course materials on media forensics often present provenance as a more robust solution than post-facto detection. (Reference to general principles taught in courses like MIT's 6.869 Advances in Computer Vision).