Agentic AI is redefining the cybersecurity landscape—introducing new opportunities that demand rethinking how to secure AI while offering the keys to addressing those challenges. Unlike standard AI systems, AI agents can take autonomous actions—interacting with tools, environments, other agents and sensitive data. This provides new opportunities for defenders but also introduces new classes of risks. Enterprises must now take a dual approach: defend both with and against agentic AI.

Building Cybersecurity Defense With Agentic AI
Cybersecurity teams are increasingly overwhelmed by talent shortages and growing alert volume. Agentic AI offers new ways to bolster threat detection, response and AI security—and requires a fundamental pivot in the foundations of the cybersecurity ecosystem. Agentic AI systems can perceive, reason and act autonomously to solve complex problems. They can also serve as intelligent collaborators for cyber experts to safeguard digital assets, mitigate risks in enterprise environments and boost efficiency in security operations centers. This frees up cybersecurity teams to focus on high-impact decisions, helping them scale their expertise while potentially reducing workforce burnout. For example, AI agents can cut the time needed to respond to software security vulnerabilities by investigating the risk of a new common vulnerability or exposure in just seconds. They can search external resources, evaluate environments and summarize and prioritize findings so human analysts can take swift, informed action.

AI-powered image generation has progressed at a remarkable pace—from early examples of models creating images of humans with too many fingers to now producing strikingly photorealistic visuals. Even with such leaps, one challenge remains: achieving creative control. Creating scenes using text has gotten easier, no longer requiring complex descriptions—and models have improved alignment to prompts. But describing finer details like composition, camera angles and object placement with text alone is hard, and making adjustments is even more complex.

Advanced workflows using ControlNets—tools that enhance image generation by providing greater control over the output—offer solutions, but their setup complexity limits broader accessibility. To help overcome these challenges and fast-track access to advanced AI capabilities, NVIDIA at the CES trade show earlier this year announced the NVIDIA AI Blueprint for 3D-guided generative AI for RTX PCs. This sample workflow includes everything needed to start generating images with full composition control. Users can download the new Blueprint today.

As enterprises increasingly adopt AI, securing AI factories—where complex, agentic workflows are executed—has never been more critical. NVIDIA is bringing runtime cybersecurity to every AI factory with a new NVIDIA DOCA software framework, part of the NVIDIA cybersecurity AI platform. Running on the NVIDIA BlueField networking platform, NVIDIA DOCA Argus operates on every node to immediately detect and respond to attacks on AI workloads, integrating seamlessly with enterprise security systems to deliver instant threat insights. The DOCA Argus framework provides runtime threat detection by using advanced memory forensics to monitor threats in real time, delivering detection speeds up to 1,000x faster than existing agentless solutions—without impacting system performance.

Unlike conventional tools, Argus runs independently of the host, requiring no agents, integration or reliance on host-based resources. This agentless, zero-overhead design enhances system efficiency and ensures resilient security in any AI compute environment, including containerized and multi-tenant infrastructures. By operating outside the host, Argus remains invisible to attackers—even in the event of a system compromise. Cybersecurity professionals can seamlessly integrate the framework with their SIEM, SOAR and XDR security platforms, enabling continuous monitoring and automated threat mitigation and extending their existing cybersecurity capabilities for AI infrastructure.

Generative AI is unlocking new capabilities for PCs and workstations, including game assistants, enhanced content-creation and productivity tools and more. NVIDIA NIM microservices, available now, and AI Blueprints, in the coming weeks, accelerate AI development and improve its accessibility. Announced at the CES trade show in January, NVIDIA NIM provides prepackaged, state-of-the-art AI models optimized for the NVIDIA RTX platform, including the NVIDIA GeForce RTX 50 Series and, now, the new NVIDIA Blackwell RTX PRO GPUs. The microservices are easy to download and run. They span the top modalities for PC development and are compatible with top ecosystem applications and tools.

The experimental System Assistant feature of Project G-Assist was also released today. Project G-Assist showcases how AI assistants can enhance apps and games. The System Assistant allows users to run real-time diagnostics, get recommendations on performance optimizations, or control system software and peripherals - all via simple voice or text commands. Developers and enthusiasts can extend its capabilities with a simple plug-in architecture and new plug-in builder.