This topic covers the growing class of AI-driven cybersecurity tools that augment threat hunting, secure code analysis, and operational governance with GPT-style models and agentic automation. As organizations put LLMs into dev, CI/CD, and SOC workflows, priorities have shifted from raw capability to observability, model governance, and data privacy — especially for regulated industries and insurance-backed risk management. Specialized cybersecurity-tuned models (e.g., purpose-built GPT variants such as Daybreak GPT‑5.5 Cyber) and embeddings/RAG pipelines are being used to detect vulnerabilities, triage alerts, synthesize exploit telemetry, and produce secure remediation guidance. Key categories and representative tools: intelligent agent infrastructure (Xilos) provides visibility into agentic activity and connected services; enterprise AI governance platforms (Monitaur) centralize policy, monitoring, validation and vendor oversight for regulated sectors; open/efficient model providers and production platforms (Mistral AI) emphasize private deployment and governance; enterprise LLM platforms (Cohere) enable private, customizable models, embeddings and retrieval for secure analysis; virtual assistants and orchestration (IBM watsonx Assistant, Microsoft 365 Copilot) embed AI into workflows for automated triage and developer productivity; no-code/low-code automation and agent builders (Anakin.ai, StackAI) accelerate deployment of scanning, reporting and remediation playbooks. Trends and operational implications: teams must combine secure model selection, fine-tuning, and private inference with observability and policy enforcement to limit data leakage and model hallucination. Insurance and compliance requirements are driving adoption of governance-first platforms. In practice, effective GPT-augmented security stacks pair model-driven analysis with agent monitoring, provenance tracking, and integration into CI/CD and ticketing systems to make secure code analysis repeatable, auditable and defensible.