The transition from AI-assisted security to AI-native defense marks a fundamental shift in how enterprise infrastructure is protected. Rather than relying on human analysts to interpret alerts, the industry is moving toward autonomous agentic systems where AI models serve as the primary defenders. This evolution is exemplified by CrowdStrike‘s introduction of SafeMind, a system designed…
On February 18, 2026, the Spanish Data Protection Agency (AEPD) released a 71-page formal supervisory authority guidance on agentic AI and the General Data Protection Regulation (GDPR). This document marks a structural shift in the European regulatory landscape: it is the first formal guidance from an EU supervisory authority to treat agentic architecture as a…
A cascading failure in agentic AI is a chain reaction where a single fault—such as a hallucination, corrupted tool output, or poisoned memory—propagates across a network of autonomous agents and compounds into system-wide harm. Unlike single-agent errors that stay bounded, multi-agent cascading failures amplify through feedback loops, coordination deadlocks, and shared state, turning minor glitches…
Deepfake fraud uses AI-generated synthetic media—fake audio, video, or images—to impersonate real people for financial gain, identity theft, or social engineering. Attackers train machine learning models on small amounts of sample footage to clone a person’s voice, face, and mannerisms, then deploy these digital forgeries to deceive victims into transferring money, sharing credentials, or trusting…
An agentic workflow is a design pattern where an AI system plans, executes, reflects on, and iteratively improves its approach to complex tasks, rather than generating a single response in one shot. The core loop—plan, act, reflect, re-plan—allows the system to use external tools, maintain context across steps, catch its own mistakes, and adapt its…
AI Safety is the interdisciplinary field focused on preventing accidents, misuse, or harmful consequences from AI systems. Learn how it differs from AI Alignment and Responsible AI.
Multimodal AI refers to machine learning models capable of processing, understanding, and generating content across multiple data modalities—including text, images, video, audio, and code—rather than being limited to a single input or output type. These models bridge different types of information to achieve richer understanding than unimodal systems.
Quantization is a model compression technique that converts high-precision numerical representations in a neural network—typically 32-bit or 16-bit floating-point numbers—into lower-precision formats such as 8-bit or 4-bit integers. This reduces model size, memory footprint, and computational cost, enabling deployment on resource-constrained hardware, at the expense of a small accuracy loss.
LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that freezes pre-trained model weights and injects small trainable low-rank matrices into each layer of the Transformer architecture. By learning only the difference between the original and adapted weights—approximated as the product of two much smaller matrices—LoRA reduces trainable parameters by up to 10,000x and GPU memory…
A foundation model is any model trained on broad data at scale—generally using self-supervision—that can be adapted to a wide range of downstream tasks. The term was introduced by Bommasani et al. (2021) at Stanford’s Center for Research on Foundation Models to describe models like BERT, GPT-3, and DALL-E that serve as a common basis…