What is AI Agent Security?

Its pending acquisition of SGNL is set to extend just-in-time, continuous authorization across cloud and SaaS, replacing standing privileges with access granted only when needed. The platform maps non-human identities to human owners, flags orphaned and over-permissioned agents, and inspects prompts and intent through Falcon AI Detection and Response to catch permission misuse. CrowdStrike Falcon Next-Gen Identity Security unifies protection for human, non-human, and AI agent identities across the hybrid identity lifecycle, combining privileged access management, identity threat detection and response, and agentic identity protection.

• Enterprise security leaders must map their AI agent security programs to emerging standards.
• Agentic AI security focuses on securing AI agents that act autonomously, ensuring their processes comply with appropriate governance and regulatory frameworks.
• Continuously track and govern agents, configurations, and actions, so innovation moves at full speed with a security program built to match it.
• Adversarial training is in ongoing development and has yet to become a standard set of training protocols.

Starting from this minimal configuration clarifies how an agent behaves before safety, structure and capabilities are added. Technical safeguards, such as least‑privilege access, sandboxing, input validation and continuous logging, help prevent misuse and limit the impact of failures. These vulnerabilities make it essential to treat agents as part of a broader attack surface rather than isolated components. Whether the goal is a simple research assistant or a fully autonomous agent system, these practices help strengthen the cybersecurity posture and protect personal data. Misconfigured agents can leak sensitive data, trigger unauthorized API calls or expose entire datasets through subtle prompt injection attacks.

• Ideal for AI agents because policies can incorporate real-time risk signals.
• From early design and threat modeling to secure development, testing, deployment and monitoring.
• They are also susceptible to manipulation through crafted inputs, such as malicious prompts or poisoned data sources, that can cause them to bypass safeguards or behave unpredictably.
• Their reliance on recursive task planning and tool integrations further complicates oversight—each step an agent takes can subtly shift context, accumulate risk, or trigger unexpected interactions.
• If these trends hold true, managing and securing AI data will become both more critical and more challenging.
• Establishing a trusted agent identity helps organizations determine which agents should be authorized, control their access, and maintain accountability for the actions they take.

NVIDIA will have no liability for failure to deliver or delay in the delivery of any of the products, features or functions set forth herein. Availability NVIDIA Open Agent Safety Platform software, including OpenShell and skills, are available through the NVIDIA developer resources page and GitHub. Red Hat runs OpenShell and DOCA, both part of NVIDIA Open Agent Safety Platform, on Red Hat AI Factory with NVIDIA, a co-engineered, enterprise-grade AI solution for building, deploying and managing AI at scale across hybrid cloud environments. Robotics leaders — such as Figure, Gecko Robotics and Skild AI — are also building with OpenShell to embed agent safety controls into autonomous systems that take action in the physical world. Scale AI is working with NVIDIA to incorporate NVIDIA Open Agent Safety Platform technologies into the agentic infrastructure layer of Scale GenAI Portfolio. Integrations with OpenShell and BlueField enable enterprises to enforce strict control over agent access through those sandboxes.

Top Cybersecurity Threats AI Agents Face Today

AI agents can execute containment actions, isolating a compromised host, revoking a service https://biocurely.com/chinese-govt-hackers-exploiting-new-atlassian-vulnerability-microsoft-says.html account, blocking a malicious IP range, and triggering a backup in seconds rather than hours. Security Operations Centers were designed for a world with a manageable number of alerts. This architecture can dramatically reduce response time but also multiplies the attack surface, a topic covered in detail in the Risks section below. In cybersecurity, this shift from copilot to autonomous agent is the difference between a system that summarizes alerts and one that investigates them, correlates signals across dozens of data sources, and executes a containment action, all without waiting for a human to click “approve.” Industry research indicates that 87% of organizations report experiencing at least one AI-driven cyberattack in the past year, a figure that underscores how rapidly adversarial AI has moved from theoretical risk to operational reality.

• Expect discovery, ownership attribution, least privilege, and just-in-time access for non-human and agentic identities.
• Rollback is harder for agents than for stateless systems because agents may have already written to memory, triggered downstream processes or passed state to other agents before the problem was detected.
• AI agents create a telemetry explosion.
• Every layer of the stack (including access control and ethical governance) needs built-in protections tailored to the dynamic nature of AI agents.
• Non-human identities (NHIs) like service accounts, API keys, OAuth tokens, and agent credentials are proliferating rapidly and have become prime attack targets.

It also requires clear policies for agent behavior, such as defined boundaries for decision-making and built-in mechanisms for human oversight. As models, tools, data sources, and workflows evolve, controls must be reviewed, tested, and improved continuously. Log prompts, tool calls, decisions, and approvals with redaction. This white paper provides practitioners with a set of controls—along with a secure by default checklist—that can help them secure AI agents in their enterprises. This expanded operational footprint introduces new risks, including unauthorized actions, data leakage, and unintended system changes, that require security considerations beyond traditional model-centric safeguards. Despite widespread AI agent adoption, only about 34% of enterprises report having AI-specific security controls in place, and fewer than 40% conduct regular security testing on agent workflows (Cisco State of AI Security, 2025).

What to look for in an AI agent security platform

This “defense-in-depth” approach, which could serve as a model for the wider industry, goes beyond traditional model alignment, adding a crucial layer of system-level security that provides assurance even if alignment is imperfect. By autonomously executing complex tasks — from cyber defence to scientific discovery and product development — these systems are unlocking a new era of productivity. Replace standing credentials with short-lived, least-privilege access while maintaining accountability for every agent action. Data, models, prompts, agents, identities, infrastructure, and interactions. AI agents can create significant business value, but their ability to reason, retrieve information, and take action also expands the enterprise attack surface.

Trusted by Teams Building AI at Scale

As AI models increasingly automate decision‑making workflows, the need for anomaly detection and risk‑mitigating controls becomes critical. This AI agent security best practices guide covers authentication, access controls, data safeguards and secure multi‑agent automation. AI agent security protects autonomous AI systems across their lifecycle, from discovery and development through identity, access, runtime behavior, and response.

Palo Alto Networks is best for large enterprises that want AI security, identity, and agentic workflows consolidated within a single platform strategy. AI Agent Security helps protect AI agents that organizations build, deploy, or use across business workflows. Common attacks include prompt injection, adversarial manipulation, data exfiltration, and supply chain compromises, all exploiting vulnerabilities in AI workflows and data handling. Legacy controls remain important, but they were not designed to govern autonomous systems operating across APIs, SaaS platforms, cloud environments, and enterprise workflows.

Linx – Best for AI-Native Identity Governance Across Humans, NHIs, and AI Agents

Astrix Security is best for security teams that want a dedicated non-human identity platform extended to AI agents, with https://www.absinthejailbreak.org/category/gadgets/ strong discovery across both sanctioned and shadow deployments. They were not designed to evaluate whether an AI agent should trust information in a document, follow instructions embedded in a web page, or execute a workflow that spans multiple systems. They can call APIs, access applications, retrieve data, execute workflows, and make decisions with limited human intervention. Once running, agents accumulate excessive privileges — many hold far more access than their workflows need — creating toxic combinations that expand the blast radius of any single compromise. They are also susceptible to manipulation through crafted inputs, such as malicious prompts or poisoned data sources, that can cause them to bypass safeguards or behave unpredictably.

Related Activities and Resources

As these agents become more capable, they also require more sophisticated safeguards. In the U.S alone, AI agents could create $2.9 trillion in economic value by 2030. This page contains forward-looking statements, including statements regarding the closing and benefits of the proposed acquisition.

AI is scaling fast. The attack surface is scaling with it.

Applying behavioral analysis to these data points allows for further security, since sudden spikes in agent activity or specific prompts allow for the immediate identification of malicious users. This identity and access management (IAM) approach is applied with relative ease to AI agents. Their reliance on recursive task planning and tool integrations further complicates oversight—each step an agent takes can subtly shift context, accumulate risk, or trigger unexpected interactions.

Also known as prompt sanitization or input validation, this practice helps insulate agents from prompt injection attacks. Context-aware authentication allows agents to retrieve data only if the user is permitted to access it. By choosing to “never trust, always verify,” ZTA reduces an attacker’s capacity for lateral movement, reducing the attack surface and buying more time for security to respond. Continuous monitoring and multi-factor authentication (MFA) help ward against threats. A common real-world example involves an attacker extracting user credentials from a compromised agent’s host system. If attackers manage to steal agent credentials, they can pose as those agents to compromise the systems to which the agent has access.