LLaMA

OSINT for the Lazy Part 19: How Generative AI Transforms Intelligence Gathering

The article examines the shift from manual OSINT practices to AI-driven workflows amid exploding data volumes. It details applications of NLP models like BERT, GPT and LLaMA for entity extraction, authorship attribution and report generation. Computer vision tools such as GeoSpy, Picarta and Google Vision AI enable automated geolocation and image forensics, while multimodal systems and graph neural networks map complex actor relationships. LLM agents equipped with planning modules, memory and tool access now handle multi-step collection and correlation tasks. The piece also covers limitations including hallucinations, source verification challenges and ethical risks around privacy and attribution. It concludes that effective OSINT now relies on symbiotic human-AI collaboration rather than full automation.

Building Secure On-Prem AI Assistants: How to Keep Corporate Data Inside Closed Contours

Many organizations hesitate to deploy AI assistants due to strict data protection rules that prohibit sending information to external clouds. The article explains how to implement AI models entirely within a company's own infrastructure, ranging from on-premise servers to fully offline laptops. It breaks down four deployment locations from public APIs to local devices and clarifies three distinct access levels: read, write, and execute. The author emphasizes that most business value comes from read-only access combined with human-in-the-loop controls for any irreversible actions. Practical recommendations include RAG over model size, quantization for local hardware, and maintaining immutable audit logs. The piece also warns that preparing clean knowledge bases often consumes more effort than the model itself.

OSINT for the Lazy Part 19: AI as a Core Tool in Modern Intelligence Gathering

The article examines how artificial intelligence has transformed OSINT from a manual discipline into a scalable, automated process capable of handling massive data volumes. It details specific AI technologies including NLP models such as BERT, GPT and LLaMA for text analysis, computer vision tools like GeoSpy and Picarta for geolocation, and multimodal systems for processing mixed data types. Machine learning techniques for anomaly detection and Graph Neural Networks are presented as methods for uncovering coordinated campaigns and hidden networks. The piece also covers LLM agents that autonomously plan and execute multi-step OSINT tasks while stressing the continued necessity of human oversight for ethical judgment and verification. Limitations, ethical risks around privacy and attribution, and the growing asymmetry between state and independent actors are highlighted as critical concerns.