AI Cybersecurity Predictions For 2026

The rapid advancement of artificial intelligence is reshaping the cybersecurity landscape, introducing new defensive capabilities while simultaneously expanding the toolkit available to cybercriminals. According to insights from Kaspersky experts, 2026 will mark a period in which large language models (LLMs) and generative AI become deeply embedded across both attack and defence strategies, forcing organisations to rethink how they manage digital risk.

One of the most visible shifts is the mainstreaming of deepfakes. Synthetic content is no longer a niche concern but a growing feature of the security agenda. Organisations are increasingly factoring deepfake risks into internal discussions, employee training programmes and incident response planning. At the same time, regular users are encountering manipulated content more frequently and are becoming more aware of its potential for abuse. As deepfakes appear in more formats (video, audio and images) they are prompting a more systematic approach to policies, education and controls.

The quality and accessibility of deepfakes are also improving. While visual realism is already advanced, audio manipulation is expected to see the most significant gains. Barriers to entry are falling, enabling even non-specialists to generate passable synthetic content with minimal effort. This combination of improving quality and wider access increases the likelihood that such tools will be exploited for fraud, impersonation and social engineering attacks.

More sophisticated real-time deepfakes such as live face and voice swapping will continue to develop, although they are likely to remain the domain of technically skilled users. While mass adoption is unlikely in the near term, these techniques pose growing risks in targeted attacks, particularly as realism improves and virtual camera technologies make manipulation harder to detect.

Efforts to establish reliable systems for identifying and labelling AI-generated content are expected to intensify. Currently, there is no universal standard for marking synthetic material, and many existing labelling methods can be easily removed or bypassed, especially in open-source environments. As a result, new technical approaches and regulatory initiatives are likely to emerge, although achieving global consistency will remain a challenge.

Another key trend is the rapid progress of open-weight models, which are closing the gap with proprietary systems in many cybersecurity-related tasks. While closed models typically include stronger safeguards and controls, open-source alternatives are increasingly powerful and widely accessible. This convergence expands opportunities for misuse, further blurring the distinction between controlled and unrestricted AI tools.

At the same time, the boundary between legitimate and malicious AI-generated content is becoming harder to define. AI is already capable of producing convincing phishing emails, fake brand identities and highly polished scam websites. As legitimate organisations adopt synthetic content for marketing and communications, AI-generated material is becoming more familiar and visually “normal,” making it more difficult for users and even automated systems to distinguish authentic content from fraudulent imitations.

AI is also emerging as a cross-cutting enabler throughout the cyberattack lifecycle. Threat actors are using LLMs to write code, automate tasks, build infrastructure and probe for vulnerabilities. As these capabilities mature, AI is expected to support multiple stages of attacks, from planning and communication to deployment and evasion. Attackers are also likely to conceal evidence of AI involvement, complicating forensic analysis and attribution.

At the same time, AI is increasingly influencing how defenders operate. Security operations centres are beginning to adopt agent-based systems capable of continuously scanning environments, identifying weaknesses and assembling contextual information for investigations. This shift reduces manual workloads and allows analysts to focus more on decision-making than data gathering. Security tools are also moving towards natural-language interfaces, enabling professionals to interact with complex systems through prompts rather than specialised queries.

Together, these developments point to a cybersecurity environment in which AI is neither inherently defensive nor offensive, but a powerful accelerator on both sides. For organisations, the challenge in 2026 will be to harness AI’s analytical potential while building resilience against the increasingly sophisticated threats it enables.

“While AI tools are being used in cyberattacks, they are also becoming a more common tool in security analysis and influence how SOC teams work. Agent-based systems will be able to continuously scan infrastructure, identify vulnerabilities, and gather contextual information for investigations, reducing the amount of manual routine work. As a result, specialists will shift from manually searching for data to making decisions based on already-prepared context. In parallel, security tools will transition to natural-language interfaces, enabling prompts instead of complex technical queries,” says Vladislav Tushkanov, Research Development Group Manager at Kaspersky.