Agentic AI encompasses artificial intelligence systems that can autonomously drive decisions and take actions to achieve complex goals with minimal human supervision. This evolution from traditional AI, which primarily analyzes data within predefined constraints, introduces significant cybersecurity risks that require rethinking existing security frameworks.
What is an AI agent and how do they work?
An AI agent is a software system that employs artificial intelligence to pursue specific goals and perform tasks on behalf of users. Unlike traditional bots that passively respond to inputs, AI agents can proactively interact with their environment, learn from feedback, and adapt their behavior to achieve their objectives.
Enhanced by the multimodal capabilities of large language models (LLMs), these agents process a wide range of data (e.g., text, voice, video, and code) and engage in sophisticated decision-making.
AI agent operational cycle
An AI agent operates through a continuous iterative cycle that enables autonomous decision-making and adaptation:
Perception
Agents ingest data via sensors, APIs, databases, or user interactions.
Reasoning
LLMs or other models analyze data to extract insights and context.
Goal setting and planning
Agents define objectives based on prompts or rules, then plan accordingly.
Decision-making
Agents evaluate possible actions and choose the most efficient path.
Execution
Agents execute actions through APIs, system calls, or UI interactions.
Learning and adaptation
Agents assess outcomes and refine their behavior over time.
Orchestration
Multiple agents can coordinate in complex environments via orchestration platforms or direct communication.
Characteristics of the new threat landscape
Expanded attack surface
Agents require access to APIs, systems, and data, multiplying points of vulnerability.
Unpredictable behavior
Learning agents can evolve in unexpected ways, making them harder to monitor.
Speed and scale of compromise
A single compromised agent can act faster and more broadly than any human.
Opaque decision-making
The “black box” nature of many LLMs complicates root cause analysis and threat response.
Security threats and risks
The autonomous and dynamic nature of agentic AI systems presents unique security threats:
Data poisoning and integrity attacks
Attackers can feed malicious inputs into an agent’s training or operational data, leading to inaccurate outputs, biased decisions, or misaligned goals.
Agent goal manipulation
Bad actors can alter an agent’s objectives through prompt injection or memory tampering, steering it toward malicious ends, sometimes without explicit system breaches.
Privilege compromise and overprivileged agents
AI agents often inherit permissions from users or systems. Without fine-grained controls, this leads to overprivileged agents that, if compromised, can perform unauthorized or destructive actions.
Tool misuse and API exploitation
Attackers can manipulate an agent’s access to external tools or APIs to trigger unintended actions, potentially turning trusted integrations into attack vectors.
Authentication and authorization bypass
AI agents often rely on stale credentials, making them prime targets for credential theft, spoofing, and unauthorized access.
Asynchronous workflow vulnerabilities
AI agents may need minutes, hours, or even days to complete tasks, requiring background operations without active user sessions and creating windows for unauthorized actions.
Identity spoofing and AI-powered phishing
Threat actors impersonate agents or users to gain unauthorized access or insert false instructions, often using AI to generate personalized, hard-to-detect phishing content.
Cascading failures and resource overload
Agents executing multiple concurrent actions can inadvertently trigger DoS conditions or amplify hallucinated responses that ripple across systems.
Repudiation and untraceability
Agent actions may go unrecorded or unanalyzed without robust logging, leaving gaps in forensic investigations and accountability.
Data exposure through retrieval-augmented generation (RAG) systems
Agentic AI systems can autonomously retrieve and act upon sensitive enterprise data through RAG without proper authorization controls, potentially exposing information beyond user permissions.
Deepfakes and synthetic media exploitation
Attackers leverage AI-generated media (e.g., audio, video, and images) to launch persuasive social engineering, disinformation, or impersonation campaigns.
Hardcoded credentials and vulnerable configurations
Poorly configured agents or those with embedded credentials become easy targets for attackers seeking unauthorized access or privilege escalation.
Agentic AI security threats vs traditional AI risks
Threat Category | Traditional AI Impact | Agentic AI Impact | Core Distinction |
Data manipulation | Training data poisoning | Dynamic memory corruption | Persistence and evolution |
Access control | Permission boundary violations | Autonomous privilege expansion | Proactive autonomy |
System integration | Single-point API abuse | Cross-system orchestrated attacks | Coordination scope |
Traceability | Black box decisions | Obfuscated autonomous actions | Accountability depth |
Resource misuse | Model overload | Intelligent resource exhaustion | Adaptive amplification |
Identity spoofing | Single identity spoofing | Multi-agent identity complexity | Identity ecosystem scale |
Agentic AI threat mitigation strategies
AI agent risk mitigation requires combining proven cybersecurity practices with AI-native controls:
Identity-first security for Non-human identities (NHIs)
Treat AI agents as privileged users by extending their identity and access management (IAM) to cover NHIs:
Enforce RBAC/ABAC to ensure least-privilege access.
Apply lifecycle management to provision, rotate, and decommission AI identities.
Monitor behavioral baselines and flag anomalies in real-time.
AI-specific authentication and credential management
Deploy authentication systems tailored for AI agents that include account linking, step-up authentication when needed, and secure token management for API access.
Use secure standards like OAuth 2.0 for token management, which automatically handles token refreshes and exchanges, while implementing secure token vaulting to prevent credential exposure.
Governance and human oversight
Establish ethical AI policies that define acceptable use, guardrails, and escalation paths.
Implement human-in-the-loop (HITL) checkpoints for sensitive or high-impact decisions.
Context-aware authorization controls
Implement fine-grained authorization for RAG systems that only allow AI agents to retrieve documents and data that users have explicit permission to access.
Secure development lifecycle
Validate training and operational data to defend against poisoning.
Follow prompt engineering best practices to prevent injection.
Harden APIs and integrations that agents rely on.
Perform red teaming and adversarial testing regularly.
Enhanced observability and forensics
Maintain immutable, signed logs for all agent decisions and actions.
Use explainable AI (XAI) approaches where feasible to improve auditability.
Microsegmentation and environmental isolation
Segment networks and limit agent access to essential data and systems.
Apply least-privilege design principles at the environment level.
Real-world examples of agentic AI security threats
In a recent survey, 23% of IT professionals reported that their AI agents had been tricked into revealing access credentials, and 80% of companies reported bots had taken “unintended actions.”
Understanding how AI agent security threats arise in enterprise environments helps organizations assess their exposure and plan defenses.
Example scenarios based on today’s emerging use cases:
Compromised marketing agent
An attacker uses prompt injection to manipulate a generative AI agent integrated into a marketing automation platform, tricking the agent into exposing internal product roadmap details and customer pricing data and causing reputational and regulatory consequences.
Overprivileged IT automation agent
An agent designed to manage infrastructure health gains inherited privileges from a superuser role. A misconfiguration causes it to trigger an unscheduled failover across regions, leading to critical system downtime.
Spoofed procurement bot
A threat actor impersonates a legitimate AI-powered purchasing agent within a multi-agent workflow. The spoofed agent bypasses weak authentication and authorizes fraudulent vendor payments.
Unauthorized customer support agent
An AI agent handling customer service inquiries gains excessive permissions to customer databases through inherited access rights. Due to inadequate authorization controls, the agent exposes sensitive customer personal information when answering routine questions, violating privacy regulations.
These examples demonstrate how agentic AI, capable of taking independent action, expands the velocity and complexity of potential attacks. Securing these systems requires a shift from reactive perimeter defense to proactive, identity-first protection.
How AI agents differ from human users
Lack of accountability: Agents, or NHIs, are tied to a piece of software, service account, workload, or autonomous agent instance, not a specific person.
Short life spans: Unlike human user accounts, agents are dynamic, ephemeral, and short-lived, requiring rapid provisioning and de-provisioning.
Non-human authentication methods: Agents rely on API tokens, JSON web tokens, mutual TLS, and cryptographic certificates.
Programmatic provisioning: Often deployed from a CI/CD pipeline, agents then need to be provisioned automatically without human interaction.
Specific permission requirements: Agents are use-case specific and require specific permissions for limited periods to minimize exposure.
Privileged information access: Agents may access highly sensitive information to accomplish goals.
Audit and remediation challenges: Agents often lack traceable ownership and consistent logging, delaying post-incident forensics.
Identity as the anchor for agentic AI security
As AI agents increasingly interface with APIs, cloud apps, databases, and enterprise tools, a unified identity security fabric becomes essential. That means treating AI agents as first-class identities for:
Unified visibility across human and non-human actors
Consistent enforcement of least-privilege access policies
Lifecycle management with provisioning, deactivation, and credential rotation for AI agents
Integration with real-time behavioral analysis and anomaly detection
Agentic AI is not just another technology that needs to be secured. It’s a shift in how systems operate. Without identity-first agentic security that includes non-human identities, organizations are vulnerable to who or what has access to critical systems.
How to future-proof against agentic AI threats
Adopt adaptive governance
AI capabilities evolve rapidly. Organizational governance must keep pace. Align with emerging standards like the NIST AI Risk Management Framework or the EU AI Act, and build policies that adjust alongside new agentic AI risks.
Continuously improve security posture
Security is a continuous process. To stay ahead of adversaries, leverage threat intelligence, automate oversight, and upskill your workforce.
Double down on Identity
The identity layer is the foundation for managing both human and machine users. Invest in unified, intelligent identity management that detects suspicious activity across all human and non-human entities.
Address emerging context-sharing standards
Integrate fine-grained access checks into emerging context-sharing standards like Anthropic’s model context protocol (MCP) so that only appropriate information reaches the right agent for the right task.
Collaborate to standardize
Security teams should share information, participate in standard-setting groups, and work across vendors to strengthen the ecosystem’s collective defense posture.
Build for resilience
Even well-secured systems may eventually face compromise. Design resilient AI systems that can rapidly detect, contain, and recover from AI-driven threats with minimal business disruption.
Ready to secure your AI-powered future? Start with identity
The fundamental paradigm shift requires moving beyond treating AI agents as privileged applications to recognizing them as autonomous digital workers that demand the same sophisticated identity lifecycle management as human employees, but with machine-speed provisioning, behavioral analytics, and dynamic authorization capabilities. Organizations that establish unified identity governance spanning both human and non-human entities will unlock the full potential of agentic AI while maintaining the granular control and comprehensive audit trails essential for enterprise security and compliance.
