The rise of agentic AI: Securing the future of autonomous systems

Updated: July 30, 2025 Time to read: ~

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:

  1. Perception

Agents ingest data via sensors, APIs, databases, or user interactions.

  1. Reasoning

LLMs or other models analyze data to extract insights and context.

  1. Goal setting and planning

Agents define objectives based on prompts or rules, then plan accordingly.

  1. Decision-making

Agents evaluate possible actions and choose the most efficient path.

  1. Execution

Agents execute actions through APIs, system calls, or UI interactions.

  1. Learning and adaptation

Agents assess outcomes and refine their behavior over time. 

  1. 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

  1. 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.

  1. Continuously improve security posture

Security is a continuous process. To stay ahead of adversaries, leverage threat intelligence, automate oversight, and upskill your workforce.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

Learn how

Continue your Identity journey