Artificial intelligence is now shaping cybersecurity faster than any other technology. Large Language Models (LLMs) are making cyberattacks easier, more scalable, and far more convincing. At the same time, they introduce new weaknesses inside organizations. As we move into 2026, AI will play a central role on both sides of the battlefield. Understanding how it changes the threat landscape is essential for every business.
AI Has Lowered the Barrier for Cybercriminals
A few years ago, launching a cyberattack required technical skill. That is no longer the case. AI tools have removed many of the entry barriers. Anyone with basic knowledge can now create malware, craft phishing messages, and generate deepfakes that look real.
The biggest driver behind this shift is the rise of Large Language Models. These models understand and generate language with a level of accuracy that was unthinkable before. This ability makes them useful not only for productivity but also for criminal activity.
Recent research from Google’s Threat Intelligence Group highlights how serious this has become. Their team found evidence that around 40 state-sponsored groups from Iran, China, North Korea, Russia, and others have already used AI assistants like Gemini. They rely on them for tasks such as:
- Gathering intelligence on target organizations
- Searching for vulnerabilities
- Writing malicious code
This gives attackers a faster, more efficient workflow, and puts more pressure on organizations to stay ahead.
LLMs and Shadow AI Are Creating New Attack Surfaces
While AI empowers attackers, it also creates new risks for businesses. Many companies are integrating AI assistants into their IT systems. These assistants can be compromised through something called prompt injection. A simple malicious instruction, often hidden in a document or message, can manipulate an AI tool into taking actions it should not.
A well-known example is the “AgentFlayer” attack discovered by Zenity in August 2025. A document embedded with invisible prompts tricked ChatGPT into stealing sensitive data from connected cloud applications. Although this specific vulnerability has been fixed, it exposed a much larger issue.
The real threat lies in shadow AI. These are tools employees use without approval or oversight. Shadow AI creates a wide, unpredictable attack surface that security teams often cannot see or control. With LLMs now handling sensitive information, the risk of data leaks continues to grow.
AI vs AI: The Next Phase of Attack and Defense
Many experts feared that AI would trigger fully automated cyberattacks. While this hasn’t happened at a large scale yet, certain automated threats are already here. Disinformation campaigns on social media are now run with AI. Researchers also warn that AI-driven adaptive malware may soon be able to change itself to bypass defenses.
Automated vulnerability scanning powered by AI is another possibility that could increase the speed of attacks.
The upside is that AI also strengthens defense. Security teams can use AI to accelerate processes in:
- Data Loss Prevention (DLP)
- Endpoint Detection and Response (EDR)
However, traditional methods don’t work as effectively with LLM-powered systems. This makes behavioral security even more important. User and Entity Behavior Analytics (UEBA) uses machine learning to spot unusual patterns in users and devices, helping detect threats that are easy to miss with conventional tools.
Why Many Organizations Are Still Hesitant
Despite the advantages, many companies are slow to adopt AI in cybersecurity. A recent survey by Sophos in the DACH region shows that only 16 percent of Swiss leaders view AI as strategically important. Germany (21 percent) and Austria (22 percent) show slightly higher adoption, but overall, the region remains cautious.
This hesitation creates a gap. Attackers are moving faster with AI than defenders, which increases the risk for organizations that delay adoption.
What Businesses Should Do in 2026
To stay prepared, organizations should focus on these steps:
- Evaluate how AI tools are being used internally
Identify shadow AI and set clear guidelines. - Strengthen defenses around LLMs
Protect against prompt injection and misuse. - Adopt behavior-based security analytics
UEBA helps detect anomalies in real time. - Train employees on AI risks
Awareness is critical with fast-evolving threats. - Use AI for defense, not just productivity
Build automation into your detection and response workflows.
Final Thoughts
AI will continue to transform cybersecurity through 2026. It lowers the barrier for attackers and introduces new vulnerabilities inside organizations, but it also provides powerful tools for defense. Companies that understand both sides of this shift and invest early in secure AI adoption will be better prepared for the challenges ahead.