Pro Security Tech Radar

Companies are increasingly using AI for cybersecurity protection – now will it work?

April 24, 2025
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  • The use of AI in business is creating an entirely new attack surface, Netwrix study claims
  • Hackers are increasingly using AI in attacks
  • AI used on company data generates intellectual property

Artificial Intelligence (AI) has made securing IT infrastructure from cyber-threats a lot more challenging, a new report from Netwrix has claimed, noting organizations are also increasingly using AI to combat the threats.

In its annual global 2025 Cybersecurity Trends Report, based on a global survey of 2,150 IT and security professionals from 121 countries, Netwix said more than a third (37%) were forced to adjust their cybersecurity approach due to AI-driven threats.

Another third (30%) said the use of AI in the office created an entirely new attack surface, while 29% said they struggle with compliance since auditors require proof of data security and privacy in AI-based systems. All of this has pushed almost two-thirds (60%) of organizations to start using AI in their IT infrastructure, and made another 30% consider implementing it, as well.

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Host of new threats

Commenting on the findings, Netwrix’s chief product officer, Jeff Warren, said businesses need to prepare for a “host of new threats” that arose with the introduction of AI.

“The data shows a rise in security incidents that are identity-driven and infrastructure-focused,” Warren said.

“Indeed, identity-driven attacks are likely to dominate even more, with crafty new ways to bypass MFA, abuse of machine-to-machine identities like service accounts and tokens, AI-powered deepfake voice and video phishing, and even synthetic identity creation at scale.”

Dirk Schrader, VP of security research at Netwrix, pointed at another fact that is often overlooked: AI trained on company data generates intellectual property and, as such, is an attractive target for cybercriminals: “It is important to secure data across the entire AI lifecycle, from ingestion to model training to monitoring API endpoints for any signs of prompt injection, abuse or model leakage,” Schrader stressed.

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