Agentic AI represents a paradigm shift in cybersecurity, offering unprecedented opportunities and novel challenges. Unlike traditional AI, it exhibits autonomous behavior, demanding a re-evaluation of security strategies. Organizations must adopt a dual approach: leveraging Agentic AI for defense and safeguarding against vulnerabilities.
Fortifying Cybersecurity Defenses with Agentic AI
Cybersecurity teams face challenges like skill shortages and alert overload. Agentic AI offers innovative solutions for threat detection, incident response, and AI security. This requires restructuring the ecosystem, with Agentic AI as a cornerstone.
Agentic AI systems perceive, reason, and act autonomously, tackling complex problems with minimal intervention. They augment human experts, enhancing their ability to protect assets, mitigate risks, and improve Security Operations Centers (SOCs) efficiency. By automating tasks and providing real-time insights, Agentic AI frees teams for strategic decision-making, scaling expertise and alleviating burnout.
Consider responding to software security vulnerabilities. Traditionally slow and labor-intensive, with Agentic AI, risk assessment for new Common Vulnerabilities and Exposures (CVEs) is reduced to seconds. AI agents rapidly search resources, evaluate environments, and generate summaries, enabling swift action.
Agentic AI also improves security alert triaging. SOCs are overwhelmed daily, struggling to distinguish critical signals. Traditional triaging is slow, repetitive, and reliant on institutional knowledge.
Agentic AI systems accelerate this by analyzing alerts, gathering context from security tools, reasoning about root causes, and acting in real-time. They also aid new analysts by codifying experienced professionals’ knowledge into actionable insights.
Key Benefits of Agentic AI in Cybersecurity:
- Automated Threat Detection: Continuously monitors network traffic and system logs to identify anomalous behavior indicative of cyber threats.
- Rapid Incident Response: Automates the process of investigating and responding to security incidents, reducing the time to containment and minimizing damage.
- Vulnerability Management: Identifies and prioritizes vulnerabilities in software and systems, enabling proactive patching and mitigation.
- Security Alert Triaging: Analyzes and prioritizes security alerts, filtering out false positives and focusing on the most critical threats.
- Enhanced Security Operations: Automates routine tasks and provides real-time insights, improving the efficiency and effectiveness of security operations centers.
Securing Agentic AI Applications
Agentic AI systems actively reason and act on information, introducing new security challenges. Agents may access tools, generate outputs, or interact with confidential data. To ensure safe behavior, organizations must implement robust security throughout the lifecycle, from testing to runtime controls.
Before deploying Agentic AI, conduct thorough red teaming and testing. Identify weaknesses in prompt interpretation, tool utilization, and input handling. Evaluate adherence to constraints, failure recovery, and resistance to attacks.
Runtime guardrails enforce policy boundaries, limit unsafe behaviors, and ensure alignment with goals. These are implemented through software enabling developers to define, deploy, and update rules governing AI agent actions. This adaptability is essential for responding to issues and maintaining safe behavior.
Essential Security Measures for Agentic AI Applications:
- Red Teaming and Testing: Simulates real-world attacks to identify vulnerabilities and weaknesses in AI systems before deployment.
- Runtime Guardrails: Enforces policy boundaries and limits unsafe behaviors during AI system operation.
- Confidential Computing: Protects sensitive data while it is being processed at runtime, reducing the risk of exposure.
- Software Supply Chain Security: Ensures the authenticity and integrity of AI components used in the development and deployment process.
- Regular Code Scans: Identifies vulnerabilities in software code and facilitates timely patching and mitigation.
Confidential Computing
Runtime protections safeguard data and agent actions during execution. Confidential Computing protects data during processing, shielding data in use. This reduces exposure risk during AI model training and inference. It encrypts data in use, ensuring that even if the underlying infrastructure is compromised, the data remains protected. This is crucial for handling sensitive data and maintaining compliance with privacy regulations. Confidential computing environments offer a secure enclave for processing data, minimizing the risk of data breaches and unauthorized access.
Secure Software Platform
The foundation of Agentic AI is the software tools, libraries, and services used to build the inferencing stack. The platform should be developed using a secure software lifecycle process that maintains Application Programming Interface (API) stability while addressing vulnerabilities throughout the lifecycle. This includes regular code scans and timely publication of security patches or mitigations. A well-maintained and secure software platform is essential for ensuring the overall security and reliability of Agentic AI applications. This involves adhering to secure coding practices, conducting regular security audits, and promptly addressing any identified vulnerabilities.
Software Bill of Materials (SBOM)
Authenticity and integrity of AI components in the supply chain are critical for scaling trust across Agentic AI systems. The AI Enterprise software stack should include container signatures, model signing, and a software bill of materials (SBOM) to enable verification of these components. This enables verification of the integrity and origin of software components, helping to prevent the use of malicious or compromised software. By maintaining a comprehensive SBOM, organizations can better manage the risks associated with software supply chains and ensure the trustworthiness of their AI systems. This involves tracking the dependencies of software components and verifying their authenticity.
Each of these technologies provides additional layers of security to protect critical data and valuable models across multiple deployment environments, from on-premises to the cloud. By implementing these security measures, organizations can ensure the confidentiality, integrity, and availability of their AI systems.
Securing Agentic Infrastructure
As Agentic AI systems become more autonomous and deeply integrated into enterprise workflows, the underlying infrastructure becomes critical. Whether deployed in a data center, at the edge, or on a factory floor, Agentic AI requires infrastructure that enforces isolation, visibility, and control.
Agentic systems operate with autonomy, enabling impactful actions. This necessitates protecting runtime workloads, implementing operational monitoring, and enforcing zero-trust principles. Runtime workload protection ensures that AI applications run in a secure environment, shielded from external threats. Operational monitoring provides real-time visibility into the behavior of AI systems, allowing for the early detection of anomalies and potential security breaches. Zero-trust principles require that every user, device, and application be authenticated and authorized before being granted access to resources.
Data Processing Units (DPUs)
DPUs, combined with advanced telemetry solutions, provide a framework that enables applications to access comprehensive, real-time visibility into agent workload behavior and accurately pinpoint threats through advanced memory forensics. By offloading security functions to DPUs, organizations can improve the performance and scalability of their security infrastructure. Telemetry solutions provide valuable insights into the behavior of AI systems, enabling security teams to quickly identify and respond to potential threats.
Deploying security controls directly onto 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. Isolating threats at the infrastructure level prevents them from spreading to other systems and minimizing the impact of security breaches. A security-everywhere architecture ensures that security controls are integrated into every layer of the infrastructure.
Confidential Computing is supported on GPUs, so isolation technologies can now be extended to the confidential virtual machine when users are moving from a single GPU to multi-GPUs. Secure AI is provided by Protected PCIe and builds upon confidential computing, allowing customers to scale workloads from a single GPU to multiple GPUs. This allows companies to adapt to their Agentic AI needs while delivering security in the most performant way. Utilizing GPUs for AI workloads accelerates processing times and improves the performance of AI applications.
These infrastructure components support both local and remote attestation, enabling customers to verify the integrity of the platform before deploying sensitive workloads. Attestation provides assurance that the platform has not been tampered with and that it meets security requirements. Local attestation verifies the integrity of the platform on-site, while remote attestation allows for verification from a remote location.
AI Factories
These security capabilities are especially important in environments like AI factories, where Agentic systems are beginning to power automation, monitoring, and real-world decision-making. AI factories are environments where AI models are developed, trained, and deployed at scale. These environments require robust security controls to protect sensitive data and ensure the integrity of AI models.
Extending Agentic AI to cyber-physical systems heightens the stakes, as compromises can directly impact uptime, safety, and the integrity of physical operations. Cyber-physical systems integrate computing, networking, and physical processes, making them vulnerable to cyber attacks. Compromises of these systems can have serious consequences, including disruptions to critical infrastructure, safety hazards, and financial losses.
Leading partners are integrating full-stack cybersecurity AI technologies to help customers bolster critical infrastructure against cyber threats across industries such as energy, utilities, and manufacturing. Full-stack cybersecurity AI technologies provide a comprehensive approach to security, protecting against a wide range of threats. These technologies can be used to monitor network traffic, detect anomalies, and automate incident response.
Key Infrastructure Security Considerations for Agentic AI:
- Isolation: Isolating Agentic AI workloads from other systems to prevent lateral movement in the event of a compromise.
- Visibility: Gaining real-time visibility into Agentic AI workload behavior to detect and respond to threats.
- Control: Implementing strict access controls and policies to limit the actions that Agentic AI systems can perform.
- Zero Trust: Assuming that no user or device is inherently trustworthy and verifying every access request.
- Attestation: Verifying the integrity of the platform before deploying sensitive workloads.
Building Trust as AI Takes Action
In today’s rapidly evolving threat landscape, every enterprise must ensure that their investments in cybersecurity are incorporating AI to protect the workflows of the future. The integration of AI into cybersecurity provides advanced threat detection, automated incident response, and proactive vulnerability management. AI can analyze vast amounts of data to identify patterns and anomalies that might be missed by human analysts.
Every workload must be accelerated to finally give defenders the tools to operate at the speed of AI. Accelerating workloads enables security teams to respond to threats more quickly and effectively. AI-powered tools can automate many of the tasks involved in incident response, freeing up human analysts to focus on more complex issues. By leveraging the power of AI, organizations can improve their overall security posture and protect themselves from increasingly sophisticated cyber threats.
The convergence of AI and cybersecurity represents a significant advancement in the fight against cybercrime. By harnessing the capabilities of AI, organizations can enhance their defenses, respond more effectively to threats, and ultimately build a more secure digital future.