AI agents are becoming widely adopted, with around four out of five organizations using or testing them, per PwC.
Given the manner in which AI agents operate, they require the same level of access as human users, which often includes interaction with sensitive data, internal systems, and APIs. And when orchestrated into multi-step workflows, the access extends across interconnected tools and environments, significantly expanding the potential attack surface.
Early incidents and research already show agents accessing unauthorized systems, exposing sensitive data, or acting beyond their intended scope, highlighting how difficult it is to control these systems with traditional security measures.
That is where Wiz AI-Application Protection Platform (AI-APP) and other security platforms come in. In the sections that follow, we break down the key security risks introduced by AI agent orchestration and explore how Wiz addresses them, while comparing its approach to those taken by other emerging security solutions in the space.
What Are the Biggest Security Risks in AI Agent Orchestration?
The core security issue with AI agent orchestration lies in the combination of autonomous decision-making and broad system access across multiple connected environments. The risks are similar to those in individual AI agents, but orchestration further amplifies their impact.
Excessive permissions are a major concern. Out of convenience, AI agents often operate with overprivileged access using service accounts, API keys, or cloud roles. In orchestrated workflows, these permissions are not limited to a single system but extend across multiple tools and services. If an attacker can manipulate the agent, they effectively gain access to everything it can reach across the entire workflow.
This spills over into another risk: data exposure. An attacker might manipulate an agent into accessing, aggregating, or unintentionally disclosing sensitive information through the very workflows they are designed to automate.
Supply chain risk in agent tools and integrations further expands the attack surface. AI agents rely heavily on external components such as plugins, frameworks, APIs, and retrieval systems.
If any of these components is compromised, attackers can influence agent behavior through the very tools and data sources it depends on.
How Wiz Helps With AI Agent Orchestration Security
AI agents are now a core part of the cloud attack surface, which is exactly why Wiz stands out as a solution that provides a unified view into how agents, identities, data, and cloud resources are connected, and the exposures that arise from those connections.
At the heart of this approach is the Wiz Security Graph, which connects these relationships to identify real, exploitable attack paths and prioritize the risks that matter most. Rather than isolated findings, Wiz surfaces exactly how different weaknesses can lead to major problems.
This is made possible by Wiz’s deep visibility into AI pipelines, integrations, and orchestration layers, where many of these risks originate. By understanding how agents interact with APIs, tools, and cloud services, Wiz can uncover unsafe configurations, excessive permissions, and risky dependencies that would otherwise remain hidden across complex workflows.
Wiz can also catch potential agent vulnerabilities in the coding and deployment phases. Wiz Code helps to surface hardcoded secrets, insecure MCP configurations and vague agent definitions, while Wiz AI-SPM finds all uses of AI to generate an AI-BOM, complete with intelligence on cloud infrastructure and resource dependencies.
Perhaps the greatest advantage of Wiz is that it runs continuously, providing run-time visibility into how agents behave as workflows execute. As soon as an agent deviates from its intended workflow or starts calling unfamiliar APIs, security teams can quickly identify and contain the risk before it escalates.
How Wiz’s Approach to Securing Agents Compares
Wiz is not the only company making waves when it comes to securing agents, so let’s compare how it fares against other leading approaches in the space.
Generally speaking while many strong solutions exist for specific use cases, they are not designed to secure AI agent orchestration across the cloud. And that is ultimately what matters for businesses. Traditional cloud security platforms such as Palo Alto Prisma Cloud and Orca Security are great at identifying misconfigurations and vulnerabilities, but they were built for relatively static environments.
When it comes to securing AI agent orchestration, surfacing individual risks is not enough. What matters is understanding how those risks connect across agent workflows, identities, and data access. Wiz maps relationships and shows how multiple weaknesses can be combined into real, exploitable attack paths rather than isolated findings.
AI-native security solutions like Lakera and Protect AI focus on model-level risks such as prompt injection and output safety. They are great at controlling how models behave, but not what agents can access or execute across the cloud. Wiz instead focuses on the full environment, connecting what the agent decides with what it can actually do.
Conclusion
Securing AI systems calls for more than protecting models. It requires control over the entire environment in which the agents operate, including their access, interactions, and the workflows they execute across the cloud.
Wiz allows organizations to unify their visibility across all the areas where agents operate, and to identify risks and misalignments before they escalate into major breaches.
Frequently Asked Questions (FAQs)
What is AI agent orchestration?
AI agent orchestration refers to connecting multiple AI agents, tools, and systems into workflows that can make decisions and execute tasks across cloud environments. These workflows often involve chaining APIs, accessing data, and triggering actions without direct human input.
Why is AI agent orchestration difficult to secure?
AI agents operate dynamically, make context-driven decisions, and interact with multiple systems using real permissions. This makes their behavior harder to predict and creates complex attack paths that traditional security tools are not designed to detect.
What are the main security risks of AI agents?
The biggest risks include excessive permissions, data exposure through agent workflows, prompt injection attacks, supply chain risks in agent tools and integrations, and unintended actions caused by manipulated inputs.
How is AI agent security different from traditional cloud security?
Traditional cloud security focuses on configurations, vulnerabilities, and static workloads. AI agent security must also account for dynamic behavior, decision-making logic, and how agents interact with tools, APIs, and data in real time.
How does Wiz help secure AI agent environments?
Wiz provides a unified view across cloud resources, identities, data, and AI workloads. Using its Security Graph, it identifies real attack paths, prioritizes risks based on context, and helps organizations secure the underlying code, the posture and runtime behavior of AI-driven environments.
