NVIDIA GPUs with Confidential Computing are now used for confidential inference in Apple’s Private Cloud Compute (PCC), as it expands beyond Apple’s data centers to Google Cloud.
Published work
AI security contributions
The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented scale. Powered by accelerated computing, AI factories enable enterprises to train, fine-tune, and deploy AI with greater speed and efficiency.
As AI models grow in complexity and regulatory scrutiny intensifies under frameworks including California’s AB-2013 and the EU AI Act, software teams face a challenge beyond delivering great code: They need to produce comprehensive, auditable model document
Autonomous AI agents are becoming more capable. Open models, Model Context Protocol (MCP)-connected tools, and portable skills are also making agents easier to extend.But scaling agent use with structural transparency and operational integrity requires more than runtime guardrails.
Bash is one of the most flexible and powerful interfaces exposed to AI agents. In the right system, a model that emits grep, curl, tar, or a shell pipeline is producing an executable action that can read files, mutate a workspace, open network connections, and chain tools together.
AI tools are significantly accelerating software development and changing how developers work with code. These tools serve as real-time copilots, automating repetitive tasks, executing tasks, writing documentation, and more.
Agents are evolving from question-and-answer systems into long-running autonomous assistants that read files, call APIs, and drive multi-step workflows. However, deploying an agent to execute code and use tools without proper isolation raises real risks—especially when using third-party cloud infrastructure due to data privacy and control.
Autonomous agents mark a new inflection point in AI. Systems are no longer limited to generating responses or reasoning through tasks.
AI is moving from experimentation to production. However, most data enterprises need exists outside the public cloud.
GPUs play an increasingly important role in modern software. However, the heterogeneous host-device execution model and expanding software stack make GPU programs prone to memory-safety and concurrency bugs that evade static analyses.
AI has evolved from assistants following your directions to agents that act independently. Called claws, these agents can take a goal, figure out how to achieve it, and execute indefinitely—while leaving you out of the loop.
As technologies and systems become more digitalized and connected across the world, operational technology (OT) environments and industrial control systems (ICS) — from energy and manufacturing to transportation and utilities — are increasingly depending on enterprise networks and the cloud. This expands OT and ICS capabilities — but also their exposure to cyber threats.
AI coding agents enable developers to work faster by streamlining tasks and driving automated, test-driven development. However, they also introduce a significant, often overlooked, attack surface by running tools from the command line with the same permissions and entitlements as the user, making them computer use agents, with all the risks those entail.
Advances in AI architectures have unlocked multimodal functionality, enabling transformer models to process multiple forms of data in the same context.
AI is powering breakthroughs across industries, helping enterprises operate with greater intelligence and speed. As AI factories scale, the next generation of enterprise AI depends on infrastructure that can efficiently manage data, secure every stage of the pipeline and accelerate the core services that move, protect and process information alongside AI workloads.
As the scale and complexity of AI infrastructure grows, data center operators need continuous visibility into factors including performance, temperature and power usage. These insights enable data center operators to actively monitor and adjust data center configurations across large-scale, distributed systems — validating that these systems are operating at their highest efficiency and reliability.
Built on open-source models, today’s AI agents can be tailored for unique workflows and business needs to boost productivity and return on investment.
AI-driven applications are evolving from passive tools to agentic systems that generate code, make decisions, and take autonomous actions. This shift introduces a critical security challenge.
AI factories continue to grow at unprecedented scale, processing structured, unstructured and emerging AI-native data. With demand for trillion-token workloads exploding, a new class of infrastructure is required to keep pace.
Developers are increasingly turning to AI-enabled tools for coding, including Cursor, OpenAI Codex, Claude Code, and GitHub Copilot. While these automation tools can enable faster development and reviews, they also present an expanding attack surface for threat actors.
Over the last several years, the NVIDIA AI Red Team (AIRT) has evaluated numerous and diverse AI-enabled systems for potential vulnerabilities and security weaknesses before they reach production. AIRT has identified several common vulnerabilities and potential security weaknesses that, if addressed during development, can significantly improve the security of LLM-based applications.
The Common Vulnerabilities and Exposures (CVE) system is the global standard for cataloging security flaws in software. Maintained by MITRE and backed by CISA, the program gives each vulnerability a unique ID and description for developers, vendors, and defenders to communicate clearly and act quickly on known risks.
Beginning August 11, 2026, the RAPIDS brand transitioned to CUDA-X. All library functionality remains the same.
AI-powered applications are introducing new attack surfaces that traditional security models don’t fully capture, especially as these agentic systems gain autonomy. The guiding principle for the evolving attack surface is clear: Assume prompt injection.
Autonomous vehicle (AV) technology is rapidly evolving, fueled by ever-larger and more complex AI models deployed at the edge. Modern vehicles now require not only advanced perception and sensor fusion, but also end-to-end deep learning pipelines that enable comprehensive environment understanding, multimodal fusion, and real-time decision making—all processed entirely onboard.