What Is AI, Really? A Cybersecurity Perspective Before We Talk About Securing It

2026-07-20 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Novice, medium

Summary

This article clarifies the fundamental definitions of artificial intelligence (AI) from a cybersecurity viewpoint, distinguishing between AI as a broad field, Machine Learning (ML) as its foundation, Large Language Models (LLMs) as a specific ML type for language, and Generative AI as the category for new content creation. It also addresses Artificial General Intelligence (AGI), noting the disagreement among leaders like Anthropic CEO Dario Amodei and Google DeepMind CEO Demis Hassabis on its definition and timeline, with Hassabis predicting its arrival in "a few short years" by July 2026. The core concern highlighted is that AI capability is scaling faster than oversight, a sentiment echoed by Amodei (2025), who calls for "serious and binding regulation," and Geoffrey Hinton (2023), who notes lobbying against guardrails and models' improved deception capabilities. This piece serves as the first in a series exploring AI's attack surface.

Key takeaway

For IT and cybersecurity professionals assessing AI integration risks, recognize that the rapid evolution of AI capabilities, particularly in reasoning and deception, is outstripping current governance and security frameworks. You should prioritize understanding the precise definitions of AI, ML, LLMs, and Generative AI to accurately scope threats. Prepare to address emerging attack surfaces like prompt injection and supply-chain risks, as industry leaders themselves are calling for urgent, binding regulation and robust testing of frontier models before deployment.

Key insights

The rapid advancement of AI capabilities is outpacing governance and security, creating significant risks.

Principles

• AI terms need precise qualification.
• Capability scaling outpaces oversight.
• Industry leaders advocate binding regulation.

Topics

• AI Security
• AI Governance
• Large Language Models
• Generative AI
• Cybersecurity Risk
• Artificial General Intelligence

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Security Engineer, Security Engineer, IT Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.