As datasets get bigger, ensuring data security and integrity becomes increasingly important. Cryptographic techniques, such as inclusion proofs, data-integrity checks, consistency validation, and digital signatures, are essential for addressing these challenges and protecting critical workloads.
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AI security contributions
As multimodal AI models advance from perception to reasoning, and even start acting autonomously, new attack surfaces emerge. These threats don’t just target inputs or outputs; they exploit how AI systems process, synthesize, and reason across modalities.
NVIDIA GPUs are at the heart of modern computing. They’re used across industries — from healthcare and finance to scientific research, autonomous systems and AI infrastructure.
This update introduces Poseidon2 to cuHash and a Merkle Tree API compatible with all cuHash hash functions.
Prompt injection, where adversaries manipulate inputs to make large language models behave in unintended ways, has long posed a threat to AI systems since the earliest days of LLM deployment. While defenders have made progress securing models against text-based attacks, the shift to multimodal and agentic AI is rapidly expanding the attack surface.
AI is entering a new era—one defined by agents that reason, plan, and take action. These agentic systems dynamically interact with APIs, tools, and even the physical environment, which introduces new complexity and vastly expands the AI attack surface and potential risks.
As large language models (LLMs) power more agentic systems capable of performing autonomous actions, tool use, and reasoning, enterprises are drawn to their flexibility and low inference costs. This growing autonomy elevates risks, introducing goal misalignment, prompt injection, unintended behaviors, and reduced human oversight, making the incorporation of robust safety measures paramount.
While speech AI is used to build digital assistants and voice agents, its impact extends far beyond these applications. Core technologies like text-to-speech (TTS) and automatic speech recognition (ASR) are driving innovation across industries.
As one of the world’s largest emerging markets, Indonesia is making strides toward its “Golden 2045 Vision” — an initiative tapping digital technologies and bringing together government, enterprises, startups and higher education to enhance productivity, efficiency and innovation across industries. Building out the nation’s AI infrastructure is a crucial part of this plan.
In today’s data-driven world, security isn’t just a feature—it’s the foundation. With the exponential growth of AI, HPC, and hyperscale cloud computing, the integrity of the network fabric is more critical than ever.
The NVIDIA DOCA framework has evolved to become a vital component of next-generation AI infrastructure. From its initial release to the highly anticipated launch of NVIDIA DOCA 3.0, each version has expanded capabilities for NVIDIA BlueField DPUs and ConnectX SuperNICs, enabling unprecedented AI platform scalability and performance.
In today’s fast-paced IT environment, not all incidents begin with obvious alarms. They may start as subtle, scattered signals, a missed alert, a quiet SLO breach, or a degraded service that slowly impacts users.
Imagine you’re leading security for a large enterprise and your teams are eager to leverage AI for more and more projects. There’s a problem, though.
Cisco and NVIDIA are helping set a new standard for secure, scalable and high-performance enterprise AI. Announced today at the Cisco Live conference in San Diego, the Cisco AI Defense and Hypershield security solutions tap into NVIDIA AI to deliver comprehensive visibility, validation and runtime protection across entire AI workflows.
Modern data centers depend on Baseboard Management Controllers (BMCs) for remote management. These embedded processors enable administrators to reconfigure servers, monitor hardware health, and push firmware updates—even when systems are powered off.
Financial losses from worldwide credit card transaction fraud are projected to reach more than $403 billion over the next decade.
Note: This blog post was originally published on Oct. 28, 2024, but has been edited to reflect new updates.
LLM Streaming sends a model’s response incrementally in real time, token by token, as it’s being generated. The output streaming capability has evolved from a nice-to-have feature to an essential component of modern LLM applications.
As modern enterprise and cloud environments scale, the complexity and volume of network traffic increase dramatically. NetFlow is used to record metadata about the traffic flows traversing a network device such as a router, switch, or host.
When interacting with transformer-based models like large language models (LLMs) and vision-language models (VLMs), the structure of the input shapes the model’s output. But prompts are often more than a simple user query.
The age of passive AI is over. A new era is beginning, where AI doesn’t just respond—it thinks, plans, and acts.
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.
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.
NVIDIA and the PyTorch team at Meta announced a groundbreaking collaboration that brings federated learning (FL) capabilities to mobile devices through the integration of NVIDIA FLARE and ExecuTorch.
As more enterprises integrate LLMs into their applications, they face a critical challenge: LLMs can generate plausible but incorrect responses, known as hallucinations. AI guardrails—or safeguarding mechanisms enforced in AI models and applications—are a popular technique to ensure the reliability of AI applications.