The Double-Edged Sword of AI in Cybersecurity: Threats, Defenses & the Dark Web Insights Report 2025

Check Point Research’s latest AI Security Report 2025 reveals a rapidly evolving cybersecurity landscape where artificial intelligence simultaneously presents unprecedented threats and defensive capabilities.

The comprehensive investigation, which included dark web surveillance and insights from Check Point’s GenAI Protect platform, uncovers how AI technologies are being weaponized by threat actors while also enhancing security researchers’ capabilities.

Advanced AI Threat Evolution

The foundations of digital identity are increasingly vulnerable as AI-powered technologies enable sophisticated impersonation attacks at scale.

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Deepfake audio and video, once requiring significant resources, have become commoditized, lowering barriers for malicious actors to launch convincing social engineering campaigns.

Traditional identity verification systems now face unprecedented challenges as synthetic identities become indistinguishable from authentic ones.

LLM poisoning has emerged as a critical vulnerability in the AI ecosystem. While major providers implement strict data validation, attackers have successfully targeted open-source platforms, with over 100 compromised models uploaded in a software supply chain attack pattern.

The Russian-affiliated “Pravda” disinformation network generated 3.6 million propaganda articles in 2024, with leading chatbots echoing these narratives in 33% of responses-demonstrating how adversaries manipulate AI systems at scale.

Cybercriminals are leveraging AI across multiple stages of the malware kill chain. From generating ransomware scripts to building info-stealers, AI enables rapid scaling of attacks even by less-skilled operators.

Dark web monitoring revealed actors using advanced chatbots to optimize malicious code, including methods for extracting credentials directly from Windows event logs.

Security Research Transformation

AI is revolutionizing defensive security operations by accelerating threat hunting and vulnerability discovery.

Large language models integrated into big data pipelines can detect advanced persistent threat (APT) tradecraft across massive datasets, analyzing language patterns to flag impersonation attempts and deceptive techniques.

The automation of traditionally labor-intensive processes-such as extracting tactical techniques and procedures (TTPs) from intelligence reports-has streamlined response workflows.

Security researchers have successfully employed LLMs to decompile and analyze malware, identifying malicious behavior even when traditional detection rates are low.

Emerging frameworks like CrewAI and Autogen allow LLMs to interact directly with systems, assisting in vulnerability research by enabling AI to use external tools and engage in complex analytical conversations.

This automation frees human analysts to focus on higher-order tasks such as adversary profiling and strategic decision-making.

Enterprise Risk Landscape

AI services are now used in at least 51% of enterprise networks monthly, according to recent data.

These interactions occur both directly through chatbots like ChatGPT (used in 37% of networks) and indirectly via AI-powered tools such as Microsoft Copilot (27%), Grammarly (25%), and DeepL (18%).

The proliferation of AI tools has introduced significant data leakage risks, with 1 in 80 prompts (1.25%) sent from enterprise devices to GenAI services containing highly sensitive information.

More concerning, 7.5% of prompts (1 in 13) include potentially sensitive data such as internal communications, financial details, and intellectual property.

As organizations increasingly integrate AI technologies, the balance between leveraging AI’s capabilities and mitigating its risks becomes crucial.

The dual nature of AI-enhancing both offense and defense in the cybersecurity realm-necessitates robust safeguards, comprehensive governance, and continuous threat intelligence to navigate this evolving landscape securely.

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