Deepfakes: Risks, consequences, and best practices for secure apps

Overview

AI-generated videos or audio are used to impersonate individuals for social engineering attacks. Deepfakes leverage artificial intelligence to create realistic but fraudulent representations of individuals. Attackers use these convincing videos or audio clips to impersonate executives, colleagues, or trusted figures. Common goals include tricking employees into transferring funds, sharing sensitive data, or undermining the credibility of a target.

AI-generated videos or audio are used to impersonate individuals for social engineering attacks. Deepfakes leverage artificial intelligence to create realistic but fraudulent representations of individuals. Attackers use these convincing videos or audio clips to impersonate executives, colleagues, or trusted figures. Common goals include tricking employees into transferring funds, sharing sensitive data, or undermining the credibility of a target.

Risk factors

Deepfakes can arise from:

• Lack of tools to verify the authenticity of audio or video.
• Over-reliance on video conferencing and digital communications.
• Minimal user awareness of deepfake threats.
• Expansion of tools that reduce barriers to creating and distributing deepfakes.

Consequences

If an attacker successfully exploits deepfakes, the following could happen:

• Financial loss: Funds may be transferred based on fraudulent communications.
• Reputation damage: Targets may face credibility issues from manipulated content.
• Data theft: Sensitive information may be disclosed under false pretenses.
• Legal risks: Creating or distributing deepfakes may lead to civil and criminal issues.

Solutions and best practices

To mitigate the risks associated with deepfakes, organizations should implement the following security measures:

• Verification protocols: Implement multi-step authentication for sensitive actions, regardless of media.
• Detection tools: Use AI tools to analyze and detect deepfake content.
• Awareness training: Educate users on identifying and questioning unusually urgent or suspicious communications.
  
  

Further reading

• The future of AI in cybersecurity: Why nothing really changes