AI Agent

AI agents represent a paradigm shift away from traditional (passive) AI tools to autonomous systems that can perceive, decide, and act on behalf of users. Unlike widely popular generative AI platforms that only respond after being prompted, AI agents act independently to achieve targeted objectives by decomposing complex tasks, gathering information, and executing multi-step workflows.

According to Gartner, 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. And by 2029, agentic AI is projected to autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs.

The rapid adoption of this technology comes with notable risks related to data access, decision-making authority, and autonomous behavior that security teams need to understand and control. As agents transition from experimental staging to production environments, organizations will need to develop AI governance frameworks that account for systems capable of independent action with broad access to enterprise data and tools.

An AI agent is an autonomous artificial intelligence system that perceives its environment, makes decisions based on defined goals, and acts to achieve those goals, all without direct human oversight at any point in the process. AI agents operate over many different interactions. They maintain contextual information and coordinate workflows that cross applications and data sources. In turn, they combine language models with access to tools, memory systems, and planners to accomplish large-scale tasks.

The autonomy spectrum ranges from assistive agents that provide recommendations requiring human approval to take action to fully autonomous agents that make decisions and take action independently. Most enterprise AI agents exist in some middle ground. The majority of agents perform routine tasks autonomously but escalate edge case scenarios or high-risk decisions to human operators. The actions of the agents depend on the level of authority granted, the tools provided, and any limitations defined by governance policies.

Task orchestration is one of the primary capabilities that define AI agents. Instead of simply answering a question, an agent breaks down an objective into subtasks, determines the correct sequence of task execution, invokes the correct tools or APIs, and synthesizes the results. This dynamic orchestration occurs as the agent adapts its methodology based on the intermediate results and/or environmental feedback.

Agentic AI operates in a continuous cycle of observation, decision-making, and action based on what it sees. An AI agent perceives its environment through various means (e.g., monitoring events, accessing data feeds, etc.) and/or from external requests. Following the perception phase, the AI agent uses decision logic to evaluate all perceived information against established objectives, then determines the best course of action. Possible courses of action may include querying databases, making API calls, creating content, or initiating workflow processes in related systems.

Tasking and execution are the primary ways that an AI agent works. For instance, if an agent were given a goal, like “look into this security alert,” they would break it down into smaller tasks, like “collect logs,” “correlate events,” “check threat intelligence,” and “document findings.” Then, they would do each of those tasks in the order that made the most sense based on how they were connected and what resources were available.

When an agent connects to internal data sources and systems, its attack surface and overall exposure to cyber threats grow. For instance, an agent could access databases, ask questions to business apps, and work within a company’s security tools. If not properly controlled, an agent could leak sensitive information, do things without permission, or be used to launch SQL injection attacks.

AI agents will soon become the new insider threat. “By 2026, autonomous copilots may surpass humans as the primary source of data leaks,” predicts Ravi Ithal, Proofpoint’s Chief Product and Technology Officer, AI Security. “Enterprises are rushing to roll out AI assistants without realizing they inherit the same data hygiene issues already present in their environments,” he adds.

Fully autonomous agents can act without human intervention. But the degree of an AI agent’s autonomy determines the level of risk it poses. Human-in-the-loop agents require approval before taking action and therefore reduce risk while reducing the speed with which the task(s) can be accomplished. Feedback and learning enable some AI agents to learn and thus improve their performance over time adaptively. However, this adaptation also adds new compliance and security risks.

AI agents are gaining traction as enterprises seek competitive advantages through automation and intelligent decision-making. Both business imperatives and technology enablers drive adoption:

• The imperative to increase productivity and improve efficiency has driven the use of agents to automate and coordinate complex workflow processes that would previously have been done manually across multiple systems. This allows employees to focus on high-value-add activities.
• Digital transformation within an organization creates a sense of urgency to transform its operations so they can leverage native AI capabilities. Organizations using agents can be faster and more responsive than those using traditional forms of automation.
• Competition pressure intensifies as early adopters earn an advantage. “Use cases are expanding rapidly across industries, with Deloitte’s survey revealing 74% of companies plan to deploy agentic AI within two years,” reports Costi Perricos, Global GenAI Business Leader at Deloitte.
• Multi-cloud and API ecosystems provide the integration infrastructure agents need to operate across enterprise systems. Modern API-first architectures enable agents to seamlessly orchestrate actions across disparate platforms.
• Multi-cloud and API ecosystems give agents the tools they need to work across different business systems. Modern API-first architectures let agents easily coordinate actions across different platforms.
• Increasing data volumes and the need for real-time processing are growing too fast for human processing to keep up. Agents can monitor data streams around the clock, spot patterns, and respond to events instantly. They can do this faster than manual workflows.

AI Agent impacts a wide variety of professionals throughout an organization, each of whom has distinct priorities, risks, etc., related to their roles in the organization:

• CISOs are most interested in ensuring that there is adequate governance, risk mitigation, and secure orchestration in place. It’s their duty to ensure agents are taking appropriate action, providing audit trails of autonomous decisions, and access controls to limit their authority. Agents introduce new attack vectors, which require strategic security frameworks.
• SecOps and IR leaders see agents as an opportunity and a risk. Agents can accelerate incident triage and automate response workflows. But if compromised, they can amplify risk. SecOps/IR rely on monitoring capabilities to track agent behavior and detect anomalous actions.
• CIOs and CTOs drive strategic adoption of agents while managing platform governance. They focus on integrating agents with existing infrastructure, establishing standards for agent deployment, and balancing innovation speed with architectural coherence across the tech stack.
• Compliance and risk teams require regulatory coverage and decision traceability. These teams require ample documentation to prove that agents are operating within legal boundaries and that audit trails detail how autonomous decisions were reached.
• Business unit leaders are seeking productivity gains and ROI from automation. They’re interested in how agents deliver target outcomes while reducing manual effort in operations, revenue generation, and customer support.
• IT and platform admins will be responsible for deployments, access management, and agent lifecycle management. They manage the intricate details and duties, provide agent credentials, monitor resource consumption, and manage version updates across agent populations.

AI agents are unique in both their design and the type of task they’re intended for. Below are some of the most common types and use cases that meet today’s organizational needs.

Task Specific Agent

A task-specific agent is designed to perform very narrow, well-defined tasks, such as document processing, data extraction, and scheduling coordination. Because of their narrow focus, these agents typically work best in repetitive workflows with clearly defined input and output processes. Organizations can leverage task-specific agents to enhance their data enrichment efforts by augmenting security alerts with threat intelligence or extracting structured data from unstructured documents.

Conversational Agent

A conversational agent combines chat interfaces with action capabilities. Unlike a simple chatbot, these agents can take action based on natural-language requests. AI agents in customer support can resolve tickets by querying knowledge bases, updating records, and coordinating with back-end systems while maintaining conversational context.

Multi-Agent System

A multi-agent system consists of several specialized agents that work together to achieve a particular goal or a set of objectives. Each agent is an expert in a particular domain, and they work together by sharing information and assigning subtasks to one another. In SecOps, multi-agent architectures are used to oversee alert monitoring (one agent), analyze threats (another agent), and coordinate fixes across several tools (a third agent).

Autonomous Decision Agent

An autonomous decision agent is granted the capabilities to function independently within pre-defined boundaries. This type of AI agent evaluates conditions, makes decisions, and executes actions without direct human oversight. Examples of this type of agent include automated incident triage agents that evaluate security alerts, correlate related events, assign severity scores, and route cases to the appropriate team(s) without human intervention.

Human Hybrid AI Agent

Hybrid agents augment human decision-making but don’t replace it. They analyze information, generate recommendations, prepare actions for approval, etc. Hybrid agents are used in service desks to analyze requests, provide suggestions on which team(s) and resources would be best suited to handle the request, and draft responses for agents’ review before sending them to the requestor.

“As enterprises integrate agentic AI tools into workflows, these systems themselves will become prime targets, exploited for the valuable data and access they hold,” says Selena Larson, Staff Threat Researcher at Proofpoint. In turn, organizations face significant risks in deploying autonomous agents across enterprise environments.

Security Threats

Enterprise security threats are associated with unauthorized action, misuse of privileges, and lateral movement if an attacker can compromise one of the agents. CISOs are concerned that when agents have access to the entire production environment, they become attractive targets for an attacker who wants to traverse their network to other areas of the organization.

Compliance Risks

When agents don’t adhere to the required control measures for accessing sensitive data or fail to properly document their activities, it can lead to enterprise compliance risks. In order for compliance personnel to demonstrate that agents adhered to the regulations regarding the use of protected data, they need auditable trails of activity.

Decision Traceability

Agents that make autonomous decisions yet don’t provide explanations of how they were made can cause decision traceability issues. When agents operate in black-box mode, business and compliance teams typically can’t demonstrate why the agent took certain actions.

Model Drift and Governance Gaps

As agents continue to operate, model drift and governance gaps develop. The reason for this development is that agents’ behaviors evolve, but they are not being monitored. As such, IT administrators are challenged to identify which agents have deviated from their intended operation or have circumvented existing policies.

Shadow Agents and Unsanctioned Bots

As teams use experimental agents (often referred to as shadow agents) in unsanctioned ways, agents proliferate. Once again, SecOps can lose visibility into agents and the extent of their access.

Escalation and Chaining Risk

Escalation and chaining risk amplify the potential negative consequences when an agent initiates a chain reaction across multiple interconnected systems. An agent’s failure to operate correctly may cause failures to cascade across critical workflow processes.

Data Quality and Hallucination Risk

When agents generate false data or make decisions based on false reasoning, it undermines reliability and creates data quality and hallucination risk.

Organizations can reduce risk and maximize value by following structured approaches to agent deployment:

• Identify your organization’s use case(s) and create a governance plan before deploying an agent. Determine what problems you want an agent to help solve, and define how you will assign ownership and set boundaries and approvals before deploying an agent.
• Assess the level of risk associated with each activity, and decide whether an activity is allowed to operate autonomously or if it requires human-centric security and oversight (e.g., high-risk decision-making).
• Authenticate agents as you would users, and provide them with unique credentials that follow least privilege principles, and integrate agents into your organization’s identity management systems (e.g., Active Directory, Azure AD, Okta). Agents should be able to authenticate and authorize themselves based on their assigned roles and responsibilities.
• Develop fail-safe and rollback procedures. Integrate protocols to stop an agent’s operation in the event of an error, and develop procedures to revert to previous states in the event of a mistake (i.e., a circuit breaker).
• Continuously monitor agent performance and error rates. Collect data on how well agents complete tasks, make accurate decisions, and how often the systems experience errors. This will enable you to quickly identify when an agent begins to behave erratically and to take corrective action.
• Collect logs that include all data related to what an agent has accessed, what decisions were made by an agent, and what actions an agent took. Logs collected in this manner provide evidence for regulatory audits and incident investigations.
• Provide training to business users about the capabilities and limitations of agents, as well as who to contact when an issue arises that cannot be resolved through an agent. Education provides the clarity needed to prevent misuse and avoid unrealistic expectations of an agent’s abilities.
• Engage security, legal, and compliance professionals during the design phase of an agent project, and continue to engage these teams throughout the deployment process. Doing so allows your organization to address regulatory requirements and risks proactively, rather than after the fact.

Proofpoint enables enterprises to monitor and control agentic AI by providing a cross-platform view of all interactions an agent has within an organization’s systems (logging). Using risk signals generated from both user and agent behavior, Proofpoint identifies anomalous interactions that may indicate a breach or a violation of organizational policies. The integration of compliance monitoring frameworks ensures agent activity complies with regulatory requirements and organizational policies. As such, telemetry provides governance teams with the audit trail and supporting documentation necessary to demonstrate oversight, perform investigations, and document the appropriate deployment of AI agents across the enterprise. Contact Proofpoint to learn more.