Most AI-enabled social engineering can still be addressed through existing defenses, but synthetic media attacks require organizations to adapt those defenses and stop treating a familiar face or voice as proof of identity.

Many uses of AI in social engineering, including personalizing phishing messages, building fraudulent websites, and automating responses, make established techniques faster, cheaper, and easier to scale. Although organizations must adapt their defenses to address the volume and sophistication of these threats, current evidence indicates that established security controls, such as filtering, verification procedures, and repeated training, still reduce the success of these attacks.

That said, synthetic media such as deepfakes and voice alteration present an exception. Synthetic media weakens the audiovisual and biometric signals that people and identity systems previously treated as evidence of legitimate identity. Research has found that both people and detection systems struggle to reliably identify deepfakes, especially those presented outside of controlled settings. As a result, defenses that rely on recognizing a familiar voice, face, or identity document are often insufficient on their own.

This distinction matters. Treating all AI-enabled threats as equivalent risks gives organizations a false sense of security while leaving them vulnerable to attacks that existing controls fail to prevent.