By Kevin Townsend, Security Week
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From defending networks to enabling attacks, artificial intelligence is changing every aspect of cybersecurity. Here’s what dozens of experts say security leaders need to understand now. T
To better understand the current state of artificial intelligence (AI) in cybersecurity, SecurityWeek spoke with dozens of security practitioners, researchers, vendors, analysts, and AI experts. The result is a comprehensive snapshot of how AI is being used across the security landscape today.
Organized into five key topic areas, this report examines the role of AI through multiple lenses: whether it can be trusted, how organizations are using it, how it can be misused by legitimate insiders, how it is being exploited by cyber adversaries, and where the technology is likely headed next.
The five topics are:
- Generative AI (gen-AI)
- Agentic AI
- Shadow AI
- Machine learning (ML)
- Artificial general intelligence (AGI)
Taken together, these perspectives provide a practical assessment of AI’s opportunities, risks, and likely evolution in cybersecurity.
Generative AI
Generative AI (gen-AI) is the bedrock of contemporary AI, although it is technically and potentially born out of earlier machine learning (ML, see below).
It does what it says: it generates new content (most commonly text) from an AI model (most usually a large language model or LLM). Chatbots are the users’ interface to the LLM, enabling questions (known as prompts) to be applied and responses received in natural language, and answers to be received in natural language. Chatbots are the interface, and LLMs are the reasoning engine. For most people in most direct use the two seem inseparable – just one big gen-AI application.
“Gen-AI trains on massive data sets, learns statistical and relationship patterns, and then uses those patterns to synthesize original output from a prompt,” explains Ahmad Shadid, co-founder and CEO at ORGN.com. This is important. It does not create factually correct answers to prompts; it predicts probable answers based on the relationship patterns it has learned – but it does create linguistically correct and compelling responses.
Four deep learning architectures power the training for modern gen-AI variants. Transformer architecture (the ‘T’ in GPT and BERT) is used for the LLMs such as ChatGPT, BERT and Claude.
Diffusion training generates the variants that focus on creating high quality images and also audio and video. Fundamentally, this process starts with random noise. Mathematically (guided by the user’s prompt) it reduces and reshapes the noise into the required clear result. Diffusion reverses the process of destruction. The generated result is again based on probability – in this case, the probably correct distribution of pixels.
Classic diffusion is evolving into diffusion transformer technology (Sora) and ‘flow matching’ (DALL-E 3 and Midjourney) which can be described as next-gen diffusion.
Generative adversarial networks (GANs) are trained via two adversarial networks locked in a feedback loop. One creates fake data, while the other learns to detect flaws by repeatedly suggesting flaws and feeding them back to the creation. Both improve until the detector can find no more flaws in the creation.
This approach is good at creating images, video and audio, but has largely been superseded by diffusion technology for business use. However, criminals still use GAN-based simple, fast, real‑time face‑swap and voice‑clone models to create deepfakes.
The fourth architecture, variational autoencoders (VAEs), use an encoder-decoder architecture for synthetic data generation, data compression, and anomaly detection. “Their main applications are in medical imaging and molecular generation for drug discovery,” comments Shadid.
Trust in gen-AI
“Gen-AI is a prediction engine. It generates what’s statistically plausible based on patterns it has seen before,” explains Emanuel Salmona, CEO and co-founder at Nagomi Security. “This makes it good at exploration: generating exploit hypotheses, trying different inputs, and connecting a strange behavior to known vulnerability patterns,” expands Albert Ziegler, head of AI at XBOW.
“It’s a tool companies can use to automate creative labor,” adds David Karandish, CEO and founder at Capacity. And because of this, “It is becoming closely embedded into security teams’ workflows, from summarizing incident reports to helping draft response plans,” continues Devvret Rishi, general manager of AI at Rubrik.