The realm of cybersecurity is undergoing a significant transformation with the emergence of Agentic AI, a pioneering technology that redefines how we secure artificial intelligence systems. Agentic AI introduces groundbreaking possibilities that necessitate a reassessment of AI security strategies while simultaneously offering solutions to overcome associated challenges. This report delves into the nuances of Agentic AI, its implications for cybersecurity, and how organizations are leveraging its potential to enhance security resilience.
At the core of Agentic AI are AI agents, which differ from conventional AI systems in their ability to act autonomously. These agents can interact with various tools, environments, other agents, and sensitive data. While this autonomy opens up new avenues for cybersecurity defense, it also ushers in novel risk categories. Organizations must now adopt a dual-pronged approach: utilizing agentic AI as a defensive tool while also safeguarding against potential threats it may pose.
Building Cybersecurity Defense With Agentic AI
In today’s cybersecurity landscape, teams are grappling with talent shortages and an overwhelming volume of security alerts. Agentic AI emerges as a beacon of hope, offering innovative avenues to bolster threat detection, response, and AI security. It necessitates a foundational shift in the cybersecurity ecosystem to fully harness its capabilities.
Agentic AI systems are equipped with the ability to perceive, reason, and autonomously solve complex problems. They serve as intelligent collaborators for cybersecurity experts, safeguarding digital assets, mitigating risks in enterprise environments, and enhancing the efficiency of security operations centers. This, in turn, liberates cybersecurity teams to concentrate on high-impact decisions, potentially reducing workforce burnout while scaling their expertise.
A prime example of this is the rapid response capability of AI agents in addressing software security vulnerabilities. They can swiftly investigate the risks associated with new vulnerabilities or exposures within seconds. By evaluating environments, searching external resources, and summarizing and prioritizing findings, AI agents empower human analysts to take informed and swift action.
Leading organizations such as Deloitte are already leveraging the NVIDIA AI Blueprint for vulnerability analysis, NVIDIA NIM, and NVIDIA Morpheus to expedite software patching and vulnerability management for their clients. Additionally, AWS has collaborated with NVIDIA to establish an open-source reference architecture for software security patching in AWS cloud environments using the NVIDIA AI Blueprint.
Moreover, AI agents significantly enhance security alert triaging. Security operations centers are often inundated with alerts daily, and distinguishing critical signals from noise is a slow, repetitive, and expertise-dependent process. Top security providers, including CrowdStrike and Trend Micro, are advancing agentic AI in cybersecurity using NVIDIA AI software. CrowdStrike’s Charlotte AI Detection Triage demonstrates remarkable efficiency by delivering faster detection triage with reduced computational resources, thereby minimizing alert fatigue and optimizing security operations.
Agentic systems streamline the entire workflow, analyzing alerts, gathering contextual data from tools, reasoning about root causes, and acting on findings in real time. They also facilitate onboarding new analysts by capturing expert knowledge from experienced analysts and translating it into actionable insights.
Enterprises can construct alert triage agents using the NVIDIA AI-Q Blueprint, which connects AI agents to enterprise data, and the NVIDIA Agent Intelligence toolkit, an open-source library that accelerates AI agent development and optimizes workflows.
Protecting Agentic AI Applications
Agentic AI systems not only analyze information but also reason and act upon it, introducing new security challenges. These agents may access tools, generate outputs with downstream effects, or interact with sensitive data in real-time. To ensure safe and predictable behavior, organizations must implement both pre-deployment testing and runtime controls.
Red teaming and testing are crucial in identifying weaknesses in how agents interpret prompts, use tools, or handle unexpected inputs before they enter production. This involves assessing how well agents adhere to constraints, recover from failures, and resist manipulative or adversarial attacks. Garak, a large language model vulnerability scanner, facilitates automated testing of LLM-based agents by simulating adversarial behavior such as prompt injection, tool misuse, and reasoning errors.
Runtime guardrails are essential for enforcing policy boundaries, limiting unsafe behaviors, and swiftly aligning agent outputs with enterprise goals. NVIDIA NeMo Guardrails software enables developers to easily define, deploy, and rapidly update rules governing AI agent behavior, ensuring consistent and safe operations in production.
Leading companies like Amdocs, Cerence AI, and Palo Alto Networks are leveraging NeMo Guardrails to deliver trusted agentic experiences to their customers. Runtime protections safeguard sensitive data and agent actions during execution, ensuring secure and trustworthy operations. NVIDIA Confidential Computing protects data while it is being processed at runtime, reducing the risk of exposure during AI model training and inference.
NVIDIA Confidential Computing is available from major service providers worldwide, including Google Cloud and Microsoft Azure, with more cloud service providers expected to offer it soon. The foundation for any agentic AI application is the set of software tools, libraries, and services used to build the inferencing stack. The NVIDIA AI Enterprise software platform is developed using a robust software lifecycle process that ensures API stability while addressing vulnerabilities throughout the software lifecycle. This includes regular code scans and timely publication of security patches or mitigations.
Ensuring the authenticity and integrity of AI components in the supply chain is critical for scaling trust across agentic AI systems. The NVIDIA AI Enterprise software stack includes container signatures, model signing, and a software bill of materials to enable component verification.
Each of these technologies provides additional security layers to protect critical data and valuable models across multiple deployment environments, from on-premises to the cloud.
Securing Agentic Infrastructure
As agentic AI systems become more autonomous and integrated into enterprise workflows, the infrastructure they rely on becomes a critical part of the security equation. Whether deployed in a data center, at the edge, or on a factory floor, agentic AI requires infrastructure that can enforce isolation, visibility, and control by design.
Agentic systems, by design, operate with significant autonomy, enabling them to perform impactful actions that can be both beneficial and potentially harmful. This inherent autonomy necessitates protecting runtime workloads, operational monitoring, and strict enforcement of zero-trust principles to secure these systems effectively.
NVIDIA BlueField DPUs, combined with NVIDIA DOCA Argus, provide a framework that enables applications to access comprehensive, real-time visibility into agent workload behavior and accurately pinpoint threats through advanced memory forensics. Deploying security controls directly onto BlueField DPUs, rather than server CPUs, further isolates threats at the infrastructure level, substantially reducing the blast radius of potential compromises and reinforcing a comprehensive, security-everywhere architecture.
Integrators also utilize NVIDIA Confidential Computing to strengthen security foundations for agentic infrastructure. For instance, EQTYLab has developed a new cryptographic certificate system that provides the first on-silicon governance to ensure AI agents are compliant at runtime. This innovation will be showcased at RSA as a top 10 RSA Innovation Sandbox recipient.
NVIDIA Confidential Computing is supported on NVIDIA Hopper and NVIDIA Blackwell GPUs, enabling isolation technologies to extend to confidential virtual machines as users transition from single GPU to multi-GPU configurations. Secure AI is further enhanced by Protected PCIe, which builds upon NVIDIA Confidential Computing, allowing customers to scale workloads from a single GPU to eight GPUs. This adaptability ensures security is maintained in the most performant manner as companies meet their agentic AI needs.
These infrastructure components support both local and remote attestation, allowing customers to verify the platform’s integrity before deploying sensitive workloads.
These security capabilities are particularly crucial in environments like AI factories, where agentic systems are beginning to power automation, monitoring, and real-world decision-making. Cisco is at the forefront of secure AI infrastructure by integrating NVIDIA BlueField DPUs, forming the foundation of the Cisco Secure AI Factory with NVIDIA to deliver scalable, secure, and efficient AI deployments for enterprises.
Extending agentic AI to cyber-physical systems raises the stakes, as compromises can directly impact uptime, safety, and the integrity of physical operations. Leading partners like Armis, Check Point, CrowdStrike, Deloitte, Forescout, Nozomi Networks, and World Wide Technology are integrating NVIDIA’s full-stack cybersecurity AI technologies to help customers bolster critical infrastructure against cyber threats across industries such as energy, utilities, and manufacturing.
Building Trust as AI Takes Action
In today’s rapidly evolving technological landscape, every enterprise must ensure their cybersecurity investments incorporate AI to protect future workflows. AI acceleration is imperative to equip defenders with the tools needed to operate at the speed of AI.
NVIDIA is at the forefront of embedding AI and security capabilities into foundational technologies for ecosystem partners to deliver AI-powered cybersecurity solutions. This emerging ecosystem will empower enterprises to build secure, scalable agentic AI systems.
Join NVIDIA at the RSA Conference to learn about its collaborations with industry leaders in advancing cybersecurity. For more information about Agentic AI and its implications, visit the official NVIDIA blog.
Understanding the nuances and potential of Agentic AI is crucial as we navigate this new era of cybersecurity. By harnessing this technology, organizations can fortify their defenses while paving the way for innovative solutions that address the ever-evolving threat landscape.
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