Detecting Deepfakes: Risks and Protective Measures for Businesses

Deepfakes are based on deep learning and neural networks. An AI model is trained on hundreds or thousands of images or audio recordings of a person. It learns facial features, expressions, voice characteristics — and can then insert these into other content.

• Video deepfakes: Faces are replaced in real time
• Audio deepfakes: Using AI Speech Synthesis, voices are synthetically replicated
• Text deepfakes: AI generates text in the style of a person

The tools for this are becoming increasingly accessible. What required specialized knowledge five years ago is now achievable with open-source software and a standard graphics card.

CEO fraud and social engineering: Deepfake voices are already being used for fraudulent calls. A supposed call from the executive board can trick employees into making transfers or sharing data.

Brand manipulation: Fake videos or statements from your executives can influence stock prices, unsettle customers, or damage partnerships.

Disinformation: In regulated industries, a strategically placed deepfake can cause significant harm — from compliance violations to reputational damage.

Detection indicators: - Unnatural facial expressions or asynchronous lip movements - Inconsistent lighting between face and background - Artifacts at hairlines, ears, or teeth - Unusual audio quality in voice recordings

Technical safeguards: - Deploy deepfake detection tools (e.g., Microsoft Video Authenticator) - Establish verification protocols for critical communications - Apply digital watermarks or content authenticity standards to media

Organizational measures: - Two-factor verification for payment approvals - Awareness training for employees - Clear processes for verifying external communications

Since 2012, arocom has supported businesses in building secure digital platforms. Trustworthiness starts with the infrastructure.