What is a Deepfake? A 2026 Guide for Security Leaders

Deepfakes now play an active role in fraud, disinformation, and reputational attacks as advances in generative AI make them easier to create and harder to detect. Organizations must adopt layered defenses and rethink trust in audio and video while preparing for emerging regulations and deepfake detection requirements.

Key takeaways from this article:

• Deepfakes are AI-generated images, audio, or video that impersonate real people — and they now play an active role in fraud, disinformation, and reputational attacks.

• Advances in generative AI (GANs, diffusion models, voice cloning) have made deepfakes easier to produce and harder to detect.

• Organizations need layered defenses: detection tools, identity-verification workflows, user training, and incident response plans.
  
• Video or voice alone is no longer a trustworthy verification method for high-risk actions.
• Security and compliance teams should begin evaluating deepfake-detection platforms and preparing for emerging regulations such as the EU AI Act and national AI-content labeling rules.
  

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Why Deepfake Security Matters Now More Than Ever

Deepfakes have quickly shifted from internet curiosities to enterprise-level threats. AI-generated voices, images, and videos now appear in wire-fraud attempts, misinformation campaigns, and executive impersonation incidents. 

The concerns are not hypothetical. In early 2024, a Hong Kong–based employee at a multinational company was tricked into sending roughly $25.6 million USD after joining a video call where the CFO and other colleagues were digitally impersonated using deepfake video and audio. This incident shows how convincingly synthetic media can bypass legacy “voice/face verification” practices.

Regulators are also reacting. In the United States, the proposed DEEPFAKES Accountability Act would require labeling synthetic media and impose transparency requirements for certain AI-generated content. In Europe, the EU AI Act introduces transparency obligations for AI systems that generate or manipulate content, including deepfakes, while the Digital Services Act (DSA) requires large platforms to assess and mitigate systemic risks such as disinformation.

For CISOs and vCISOs, deepfake readiness now intersects directly with identity security, fraud prevention, AI risk, and brand protection — alongside broader AI governance you may already be addressing with an AI risk management program.

What is a Deepfake?

A deepfake is synthetic media, typically video, image, or audio, generated by AI to imitate a real person’s likeness, voice, or behavior. Deepfakes usually fall into three categories:

• Deepfake video — face-swapping, facial reenactment, or text-to-video generation
• Deepfake audio — voice cloning from relatively small samples of speech
• Deepfake images — AI-generated headshots, doctored IDs, or manipulated documents

Unlike traditional media editing (e.g., Photoshop or basic dubbing), deepfakes are AI-generated, automated, and increasingly easy to create at scale using off-the-shelf tools and pre-trained models.

How Deepfakes Are Created

Deepfakes are produced using machine-learning models trained on real examples of a person’s face, voice, or gestures.

Common techniques include:

• Generative Adversarial Networks (GANs) — two neural networks (generator and discriminator) are trained against each other until the generator produces realistic synthetic outputs.
• Autoencoders — compress and reconstruct facial representations, enabling face-swapping and reenactment across scenes.
• Diffusion models — the newer standard behind high-quality image and video generation; they iteratively transform random noise into coherent visuals guided by prompts or reference frames.
• Voice-cloning models — speech synthesis systems that can reproduce a target speaker’s voice from relatively short audio recordings.

Attackers use these techniques to:

• Swap faces into videos
• Recreate lip movements to match new audio
• Generate fake video from a single still image
• Produce convincing cloned voicemail or live “executive” audio on calls

Deepfakes are effective not because they’re perfect, but because they’re plausible, especially in low-resolution video calls and fast-paced communication environments where recipients have little time to scrutinize details. 

Legitimate vs. Malicious Use Cases

Deepfake technology also powers legitimate applications:

Legitimate Use Cases

• Film and entertainment (de-aging actors, visual effects, realistic dubbing)
• Voice restoration for individuals who have lost speech or suffer from degenerative conditions
• Training simulations and educational avatars (e.g., realistic roleplays, historical re-creations)

However, malicious uses have grown faster and generate the most risk for security teams:

Malicious Use Cases

• Executive impersonation and payment fraud (e.g., CFO deepfake incidents)
• Political misinformation and deepfake propaganda
• Non-consensual explicit deepfake content
• Social-engineering attacks using cloned voices or video
• Synthetic ID and documentation used in fraud or account takeover

Deepfake risk, therefore, touches fraud, PR, legal, HR, and cybersecurity, making it a genuinely enterprise-wide concern, not just a technical curiosity. 

Deepfakes vs. Traditional Manipulation

Traditional media manipulation required significant time and expertise. Deepfakes change that equation:

• Automated generation — large volumes of synthetic content can be produced programmatically.
• Real-time impersonation — attackers can simulate live video calls.
• Low skill barrier — user-friendly tools and pre-trained models make attacks more accessible.
• High believability in low-res environments — many scams exploit grainy video or compressed audio where small artifacts are difficult to notice.

This is why conventional content-moderation and visual inspection often fail to keep up with deepfake campaigns. 

How Deepfake Detection Works

Detection technology is improving, but must continually evolve alongside new generative models.

Detection methods include:

• AI forensics — models analyze lighting, shadows, textures, frame consistency, and blinking or facial-movement patterns to flag potential manipulations.
• Audio forensics — tools examine frequency spectra, prosody, and cadence for artifacts typical of synthetic speech.
• Provenance systems — standards like the Coalition for Content Provenance and Authenticity (C2PA) and the Content Authenticity Initiative define ways to embed cryptographic signatures and edit histories into media so consumers can verify origin and modifications.
• Watermarking — several AI vendors and regulators are pushing for invisible or visible watermarks that identify AI-generated outputs, in line with the EU AI Act’s transparency obligations and national rules such as Spain’s AI-content labeling bill.

Reality check: No single method is enough. Organizations need layered detection (forensics + provenance), plus manual verification for high-risk decisions like large payments or policy changes.

Regulation & Compliance Landscape

Regulators are actively shaping obligations around synthetic media and AI:

• DEEPFAKES Accountability Act (U.S.) – a proposed federal bill requiring labeling of deepfake content and setting transparency rules for certain synthetic media.
• EU AI Act – the first comprehensive AI law; it requires providers and deployers of certain AI systems (including those generating “deepfakes”) to clearly disclose that content is artificially generated or manipulated.
• Digital Services Act (EU) – imposes systemic risk-management obligations on large platforms, including risks linked to disinformation and deepfake content.
• China’s Deep Synthesis Provisions – regulations on “deep synthesis” services (including deepfakes) that require labeling and impose responsibilities on providers.

Security leaders should add deepfake-related obligations and detection expectations into their GRC and communications policies, alongside broader AI governance efforts. Learn more about Rivial Security’s AI Risk Management Solution. 

Business Impact

Deepfakes introduce risk across multiple areas:

• Fraud: Attackers impersonate executives or vendors to approve payments or change banking details.
• Brand safety: A fake executive video can go viral before communications teams can respond.
• Operational disruption: Misinformation incidents require coordinated legal, PR, and security responses.
• Trust erosion: Customers and partners increasingly expect organizations to authenticate official communications and digital media.

Deepfake defense is no longer niche; it’s tied directly to enterprise resilience and overall AI risk posture. 

Evaluating Deepfake Security Tools

When reviewing detection platforms or broader AI-security solutions, prioritize:

• Detection accuracy & update cadence
• Coverage across video, image, audio, and livestream content
• API / SIEM / SOAR integrations so detection fits into your existing SOC workflows.
• False-positive handling and analyst review tools
• Support for provenance standards like C2PA
• Compliance and reporting features for regulators and auditors
• Scalability and real-time performance for high-volume or live environments

Security teams should test tools using real internal media: executive video messages, voicemail workflows, collaboration tools, vendor-intake flows, etc. This approach aligns well with how Rivial structures AI and cyber risk assessments.

Who Needs Deepfake Protection?

AI security is especially relevant for organizations with:

• High-value transactions (banks, fintech, global enterprises)
• High reputational exposure (media, entertainment, public companies)
• High disinformation risk (governments, election bodies, education)
• High reliance on virtual communication (remote and hybrid teams)

Example scenarios include:

• Blocking a fraudulent transfer initiated via a cloned CEO/CFO on a video or audio call
• Verifying a viral “leaked” political or corporate video before making public statements
• Authenticating remote employees or contractors in sensitive workflows
• Detecting tampered evidence in HR, legal, or compliance investigations.

The Future of Deepfakes

Advances in multimodal and diffusion models will make deepfakes more realistic and easier to generate. At the same time, defenses are improving through:

• Camera-level provenance signing and content credentials.
• Real-time call detection for video and voice impersonation.
• Watermarking mandates and national labeling laws (e.g., Spain’s forthcoming fines for unlabeled AI content.
• Verified content pipelines inside newsrooms, enterprises, and government

Organizations that establish verification, detection, and communication protocols today will be better positioned as deepfake attacks become more frequent and more sophisticated. 

Best Practices for Managing Deepfakes

• Verify before acting — especially for financial or sensitive requests; use callbacks and secondary channels.
• Use AI detection tools — apply automated screening to inbound video, audio, and suspicious media.
• Educate employees — train staff to pause, verify, and escalate rather than rely on “gut feel.”
• Document incidents — maintain logs and preserve artifacts for legal, compliance, and forensic review.
• Coordinate across teams — security, PR, HR, legal, and executive leadership should share ownership of the deepfake response plan.

Common Challenges and Solutions

Challenge: Rapidly evolving deepfake tools
Solution: Use vendors that retrain models regularly and track emerging generative techniques. 

Challenge: Regulatory uncertainty
Solution: Align internal policies with emerging laws like the EU AI Act, DSA, and national labeling rules (e.g., Spain’s AI content bill). 

Challenge: Public desensitization (“everything might be fake”)
Solution: Use provenance tools, transparent disclosures, and verified official channels to rebuild trust. 

Challenge: False positives
Solution: Combine automated detection with human review for high-impact decisions; treat detection scores as triage, not final judgment. 

How Deepfake Detection Platforms Simplify Response

Modern deepfake-detection and AI-risk platforms help teams quickly verify authenticity by providing:

• Automated image, video, and audio forensics
• Real-time alerts on suspicious content
• Integration with SIEM, GRC, and fraud systems
• Compliance-ready reporting for regulators and auditors
• Continuous model updates as new attack techniques appear.

Done right, AI risk management becomes part of a scalable, repeatable cybersecurity workflow. Learn more or schedule a demo with Rivial Security today.

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