Learn how the democratization of AI over the past two years has transformed today’s cyber threat landscape, accelerating attack automation and expanding organizations’ risk exposure.

In cybersecurity, artificial intelligence has become a dual-edged sword. While it can be a powerful force for good, aiding in threat discovery, it’s also responsible for a wave of increasingly dangerous threats. Cyberattackers, as early adopters of new technologies, have learned to weaponize generative AI at an alarming pace.

According to a recent study by the UK’s National Cyber Security Centre (NCSC), more than half of enterprise executives now rank AI cyber risks as among their top three organizational risks. The study concludes that within the next two years, “AI will almost certainly continue to make elements of cyber-intrusion operations more effective, leading to an increase in the frequency and intensity of cyber threats.”

The Evolving Nature of Cyber Threats

AI is being employed by attackers in various ways, evolving the cyber threat landscape at tremendous speeds. Besides leveraging deepfakes and voice cloning for impersonation attacks, cybercriminals are using AI to hunt for software vulnerabilities and create new kinds of malware that can adapt in real time to avoid detection. 

Simultaneously, the increased adoption of AI tools by organizations has soared, inadvertently creating new attack vectors for weaponized AI systems to exploit.

Deepfakes, voice cloning and personalized messaging

Deepfakes are perhaps the most worrying threat, due to their amazing realism, leading to a notable increase in attacks that mimic the voice and likeness of senior company executives. 

A high-profile example involved an employee at a large multinational company’s finance division who was tricked into joining a conference call with multiple deepfake-generated colleagues, including the company’s CFO. Convinced he was dealing with his bosses, the worker was tricked into transferring $25.6 million to cybercriminals’ bank accounts.

Deepfakes are also increasingly being used to scam consumers by impersonating friends or family members in “emergency” scenarios to solicit money. Nation-state actors, such as North Korea, have also used deepfakes to bypass HR filters and gain remote employment with western companies in an effort to steal corporate data.

Meanwhile, phishing attacks have likewise grown in sophistication. Phishing once relied on generic, error-ridden messaging developed from scratch for each campaign. Thanks to AI, modern phishing attacks are more convincing than ever, with generative AI making it easier for attackers to craft highly personalized emails and messages with far fewer spelling and grammatical errors, and injecting personal details about the prospective victim, scraped from their public-facing social media presences. 

One trending tactic is to compromise an email address and then wait for the perfect moment to “hijack” conversations, inserting a malicious message into genuine email threads. 

Automated reconnaissance and adaptive malware

Before launching an attack, cybercriminals perform reconnaissance to gather information about a target organization’s systems, users, and infrastructure. Traditionally, this was a time-consuming human task, but AI enables this work to be automated, speeding up target identification significantly.

Instead of humans, reconnaissance is now done by malicious AI agents that gather information with minimal human oversight. Examples include AI scripts that crawl corporate websites, extracting employee names and finding corresponding LinkedIn profiles to build spearphishing target lists. 

The dynamic nature of these AI agents is of particular concern, as they can be programmed to utilize different tools to scan systems for weaknesses and adapt their tactics based on the feedback they receive to avoid detection. After all, AI agents are more methodical and much faster than human reconnaissance, adapting and exploring based on discovery rather than following a static list.

Once targets are identified, attackers can unleash “adaptive malware” that changes its appearance and behavior to evade detection and improve its effectiveness over time. These malicious programs use AI to modify their code in real-time, making it harder for security systems to identify and bypass defenses.

Adaptive malware can also generate customized malicious scripts for each new target, and evolve in real-time to mimic legitimate applications and network traffic to avoid detection. Unlike traditional malware, which relies on static, pre-programmed instructions, AI’s autonomous decision-making capabilities allow malware to learn from failed attacks and refine its tactics, resulting in much higher success rates. 

Shadow AI and data leaks

Shadow AI refers to third-party AI tools like ChatGPT being used by employees without formal approval. While convenient and productive, their widespread use introduces substantial organizational risks.

A 2024 study by the U.S. National Cybersecurity Alliance revealed that 38% of employees admitted to sharing sensitive information with AI tools without employer permission. The danger is that these chatbots tend to retain the information entered by users as prompts, including sensitive financial data, design plans, emails and customer data. 

This behavior is dangerous – not only could the data be intercepted in transit, but it can also be used to train future generations of the model and reappear in subsequent generated responses.

Security professionals struggle to convey these risks to regular employees, as Samsung discovered to its horror in 2023, when headlines told of an engineer pasting proprietary code into ChatGPT to try and clean it up, and a second employee asking it to generate meeting minutes, resulting in a detailed breakdown of internal discussions.

The Impact of AI Threats on Cyber Strategies

The embrace of AI by cyberattackers, which allows them to scale and adapt tactics with alarming agility, is forcing organizations to adopt newer, more sophisticated approaches to threat detection.

A major strategic shift is underway, as cybercriminals are less interested in breaching networks directly and are instead pivoting to target identities. As a result, compromised user accounts and abused permissions have become the most common entry points. AI-generated deepfakes and voice cloning undermine existing identity-based authentication, allowing attackers to mimic executives and colleagues. 

Once the target is tricked, hackers gain valid access to protected systems, move laterally, and escalate privileges to access sensitive information without triggering alarms.

Remote work has facilitated this trend, as users authenticate from multiple locations and devices, making traditional access tools less effective. Trust can no longer be assumed based on location alone. To counteract this, organizations must deploy more rigorous identity verification tools and authentication systems that continuously evaluate who is accessing their systems – including their location, devices, and circumstances – each time access is requested.

AI is also changing the goal of cyberattacks, which are increasingly designed for persistence rather than immediate disruption. Attackers now seek long-term access, monitoring systems, mapping data flows, and harvesting user credentials while waiting for the optimum moment to strike for maximum damage, such as by targeting high-value transactions or leadership changes.

These long-dwell threats are especially dangerous, because they effectively sidestep traditional security alerts. There are no obvious signs of system failure, visible ransomware messages, or immediate service outages. The damage is done quietly via data exfiltration, strategic disruption and manipulation.

Avoiding these threats calls for a vigilant “human firewall,” a workforce that understands what’s at stake and is familiar with the latest attack patterns.

Detecting these threats, meanwhile, requires security teams to enhance visibility through sophisticated behavioral analysis tools, replacing traditional signature-based techniques. This means identifying deviations from normal patterns, such as unexpected data access, unusual login times, and abnormal system behaviors. Without this in-depth visibility, persistent threats can remain undetected for months.

The Arms Race Ahead

Cybersecurity has always been a “cat-and-mouse” game, but the growing prevalence of AI threats means we’re now entering an ever-escalating arms race, where the winners will employ the most advanced tools.

Human security teams can no longer keep pace with AI-assisted hackers on their own. Modern security tools generate endless volumes of logs and alerts, making AI-driven monitoring, and AI-enhanced security training, necessary to keep up. Simultaneously, newer AI tools are needed to continuously analyze user and system behavior, identify anomalies in real time, keep people on their toes, and surface threats before they escalate.

The 2026 cyber threat landscape has fundamentally transformed. AI-powered reconnaissance, adaptive malware, identity-based breaches and silent-persistent tactics mean traditional security models are no longer sufficient. To stay one step ahead, organizations must adopt adaptive, AI-driven security strategies focused on education, identity control, visibility and continuous monitoring.