Deepfake Trends to Look Out for in 2025

Recent innovations in deepfake technology and AI-driven applications have brought synthetic media to the forefront of cybersecurity concerns. From artificial intelligence-generated videos that replicate a public figure’s likeness to audio-based deepfakes capable of mimicking a familiar voice, the technology behind these fakes is advancing rapidly.

The implications are significant: misinformation, social engineering, and identity fraud are just a few of the outcomes that might result from these convincing simulations.

In this article, we discuss the top deepfake trends that will dominate conversations about cybersecurity in 2025. We focus on the rise of voice-based deepfakes, new detection methods, legal developments, ethical AI, and social engineering exploits.

We’ll also highlight how businesses can respond to these changes, strategies, tools, best practices, and more.

Rise of voice-based deepfakes

Increased sophistication in audio deepfakes

Voice-based deepfakes are no longer novel internet experiments. Generative AI tools, spurred by leaps in deep learning and advanced TTS (text-to-speech) capabilities, allow fraudsters to replicate voices with remarkable accuracy.

Cybercriminals can capture voice samples from interviews, podcasts, or social media clips. Once they feed these recordings into neural network–based models, they can generate AI voices that closely resemble the original speaker’s pitch, cadence, and unique mannerisms.

The potential for abuse is high. Real-time impersonation of executives, family members, or customer service representatives can lead to unauthorized transactions, data breaches, or social engineering schemes.

A McAfee survey revealed that one in four adults had experienced or known someone affected by an AI voice cloning scam, and 70% were unsure of their ability to distinguish cloned voices.

Additionally, recent studies by Synthical found that, on average, people struggle to distinguish between synthetic and authentic media, with the mean detection performance close to a chance level of 50%. They also found that accuracy rates worsen when the stimuli contain any degree of synthetic content, feature foreign languages, and the media type is a single modality.

A coordinated attacker might deploy a voice-based deepfake to manipulate or trick contact center agents, especially if the attacker has stolen personal information from data breaches. These developments illustrate why it’s becoming so urgent for organizations to recognize and address deepfake trends.

Voice phishing (vishing) becoming more prevalent

As voice deepfakes become more accurate, so do targeted attacks involving “vishing.”  According to a 2024 APWG report, vishing and smishing attacks rose 30% (in Q1 2024) compared to the previous quarter (Q4 2023). Criminals use AI-generated calls to pose as familiar voices—like a high-level executive within a company—and request urgent financial transfers or sensitive internal data.

Security teams are already facing challenges from these kinds of impersonation, and the trend, driven by affordable AI-as-a-service platforms, is expected to increase in the coming years. After all, the AI-as-a-service market was estimated at $16.08 billion in 2024 and is projected to grow at a CAGR of 36.1% from 2025 to 2030.

It’s worth exploring how infiltration might occur through contact center interactions. Traditional verification questions are less effective against criminals who blend plausible personal details with an AI-synthesized voice.

For a deeper overview of how these threats can spill over into different settings like healthcare, see our article: Understanding the threat of deepfakes in healthcare.

Advancements in deepfake detection technology

Development of AI-powered tools to identify deepfakes

On the positive side, businesses, researchers, and government agencies are dedicating considerable time to improving deepfake detection. AI algorithms can help isolate imperceptible artifacts or inconsistencies within synthetic audio.

For example, some models zero in on tonal shifts, background static, or timing anomalies that might not be obvious to human listeners. Voice-based detection solutions increasingly rely on machine learning training sets that compare thousands of human vs. synthetic voice samples.

Focusing on liveness detection helps identify the specific markers of synthetic speech and determine if it has suspicious voice patterns. Rather than relying on static checks, these solutions examine the audio stream in real time, searching for signs of robotic or artificially-generated content.

Integration of deepfake detection in cybersecurity systems

Deepfake detection is also becoming a standard consideration for modern cybersecurity frameworks. For instance, organizations that rely on multifactor authentication may add a layer of voice-based checks. If the system suspects AI-generated audio, it could require an extra step, such as confirming a known device.

Retailers, in particular, face unique challenges because voice-based impersonations can compromise everything from loyalty accounts to credit card data over the phone. Learn how retail contact centers can integrate advanced detection within a broader security system and more.

For more on best practices in multifactor approaches, check out our multifactor authentication solution, which is designed to authenticate callers without disruption.

Ethical AI and responsible deepfake development

The rise of deepfakes has also sparked a discussion around ethical AI. Researchers, non-profit organizations, and technology leaders push for transparency and accountability in AI model development.

Responsible AI frameworks encourage thorough risk assessments, data privacy considerations, and ethics boards to review how new models could be misused. Some social media platforms experiment with content labels or watermarking solutions to flag AI-generated media.

This discourse isn’t limited to theoretical debates. Media outlets and political actors are also exploring how to stop the spread of false information. Some of our resources expand more on this topic:

• Demonstrating Pindrop technology’s role in election integrity
• How deepfakes are impacting public trust in media
• Deepfakes and the escalation of political conflict

Deepfakes in social engineering

Social engineering often relies on manipulating human judgment, and deepfakes are poised to take these schemes to new levels. AI-generated voice or video can be added to email or phone-based phishing attempts to lend an air of legitimacy.

Picture an employee in the finance department receiving what appears to be a video call from a known executive instructing them to authorize a bank transfer immediately.

The threat extends beyond single interactions. Attackers may plan multi-step campaigns, with each layer adding authenticity to the ruse. As explained in our Deepfake Attacks in 2025 guide, fraudsters might gather personal details through low-level intrusion attempts and then escalate to more targeted attacks once they’ve compiled enough data.

Development of new strategies

Fraudsters consistently evolve. Organizations may face unpredictable and more aggressive strategies as they experiment with combining deepfakes, data scraping, and older techniques like spear phishing.

In some incidents, deepfake audio can be paired with spoofed emails, making the scam appear verbally and textually legitimate. Combining synthetic voice with well-researched personal information can spell trouble for organizations.

Attackers can now mimic real-time conversations, further confusing human call-handling staff. For this reason, experts strongly recommend adopting advanced authentication and deepfake detection solutions as a protective measure.

Protect yourself from deepfake attacks with Pindrop® solutions

While new technology can be daunting, solutions exist to help mitigate these evolving threats. Liveness detection is an essential approach: it pinpoints key markers in audio or video that indicate whether an actual living human or AI generates content.

Liveness detection can spot audio anomalies—areas where the voice’s tonality, breath, or resonance doesn’t match typical human patterns.

Pindrop® Pulse refines these steps by alerting your contact center agents when synthetic voices are detected—providing critical defense against emerging threats caused by the rise of AI-generated content. Learn more about Pindrop® Pulse.

Companies also benefit from a multifaceted security strategy, blending voice detection with other tools. For instance, robust identity validation processes can make it harder for attackers to rely on voice impersonation alone. Tools like multifactor authentication can prompt additional verification steps, forcing criminals to contend with multiple defenses simultaneously.

Ready to take the next step? Check out Pindrop’s deepfake detection technology.