Agentic AI security: Complete guide to threats, risks & best practices 2025

AI in the enterprise has evolved beyond simple chatbots and assistants. 

While most organizations spent 2023 and 2024 experimenting with conversational systems powered by large language models (LLMs), we're now seeing AI systems that can actually take action on their own.

These systems, known as agentic AI, extend the capabilities of LLMs. Instead of only generating responses, they leverage reasoning, memory, and tool integrations to execute tasks across business environments. 

Imagine an AI that manages sales pipelines, processes purchase orders, updates financial systems, and surfaces risk alerts to leadership, seamlessly executing tasks across business units without manual intervention. That’s the direction enterprise AI is taking.

This leap from “language-only” to “language plus action” is why agentic AI is gaining traction. According to research from the Cyber Security Tribe, 59% of organizations surveyed between December 2024 and January 2025 said implementing agentic AI in their cybersecurity operations was a “work in progress”. 

And Gartner forecasts that by 2028, 33% of enterprise software applications will embed agentic AI — compared to less than 1% in 2024.

But here’s the challenge: when AI systems move beyond generating words to independently accessing systems, chaining tools together, and making real-world decisions, the security implications shift dramatically.

Unlike traditional models, where risks are confined to inaccurate outputs or data leakage, autonomous agents introduce entirely new threat surfaces. Their ability to operate across applications, persist memory, and act without constant oversight means a single compromise can cascade across business-critical systems in ways that conventional security controls were never designed to handle.

This guide unpacks why that shift marks a paradigm change for enterprise AI security and shows how autonomous agents’ decision-making and adaptation create risks that security leaders and executives must address. 

For a structured way to map those risks and design mitigations, we’ve also distilled leading frameworks into a practical toolkit you can apply inside your own environment.

What is agentic AI security?

Agentic AI refers to AI systems with agency, meaning the capacity to make independent decisions, plan and execute actions, retain memory, and adapt in real time.

Agentic AI security is the discipline of protecting these autonomous systems from threats, ensuring they operate within defined boundaries, and preventing misuse of their decision-making, memory, and tool integrations.

Traditional AI security focused on blocking malicious inputs like prompt injection and controlling outputs. With agentic AI, the challenge is broader. Teams must secure systems that can remember past interactions, operate across multiple applications, and take actions on their own. 

The key difference is autonomy. These systems do not just respond to instructions; they decide what needs to be done and how to do it.

Key characteristics of agentic AI systems

Understanding these agentic AI system traits helps explain why they require new approaches to governance and protection:

Autonomous decision-making capabilities

Agentic AI systems can reason through problems and choose next steps without waiting for human input. This independence allows them to act continuously, but it also means errors or manipulations can compound quickly.

Multi-step reasoning and planning

These systems break complex goals into smaller tasks and execute them in sequence. They can also adapt mid-process, revising their plan when new obstacles or opportunities appear.

Tool and API integration

Agents can connect directly to APIs, databases, email systems, and business applications to carry out tasks. This ability gives them real operational power but also creates new avenues for misuse if access is not properly controlled.

Memory and state persistence

Unlike traditional AI that resets after each session, agentic systems retain memory of previous conversations and actions. This persistence enables more context-aware behavior, while also introducing risks of memory poisoning and long-term manipulation.

Real-time environmental interaction

Agentic AI can respond dynamically to changing conditions, new data, or unexpected inputs. This allows them to function effectively in live enterprise environments but exposes them to unpredictable external influences.

Self-modification and learning

Some systems can update their own prompts, tools, or strategies as they learn from experience. While this creates opportunities for continuous improvement, it also raises concerns about uncontrolled behavior or drift from intended boundaries.

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Top agentic AI security threats and vulnerabilities in 2025

With the foundations defined, the next step is looking at how agentic AI systems can be attacked in practice. 

The OWASP Agentic Security Initiative (ASI) has catalogued 15 categories of threats that map directly onto an agent’s architecture, from memory and planning to tool usage, inter-agent communication, and human interaction.

1. Memory poisoning attacks

Memory poisoning involves corrupting an agent’s short-term or long-term memory with malicious or misleading data. Unlike traditional AI models that forget after each session, agentic systems persist context across interactions, which makes memory both a strength and a liability.

2. Tool misuse 

Agentic AI derives much of its power from connecting to enterprise tools and APIs. Tool misuse occurs when attackers trick or coerce an agent into abusing those integrations, even when the agent is technically operating within its authorized permissions.

3. Privilege compromise

Privilege compromise arises when attackers exploit weaknesses in permission management to perform unauthorized actions. This often happens through dynamic role inheritance, misconfigurations, or poor boundary controls. The result is that an agent gains more access than originally intended, allowing attackers to pivot across systems.

4. Resource overload

Resource overload targets the computational, memory, or service capacities of agentic AI systems. By overwhelming these resources, attackers can degrade performance, disrupt availability, or even trigger complete system failures.

5. Cascading hallucination attacks

Cascading hallucination attacks exploit an agent’s tendency to generate plausible but false information. When unchecked, these hallucinations can spread through memory, influence planning, and trigger tool calls that escalate into operational failures.

6. Intent breaking and goal manipulation

Intent breaking occurs when attackers manipulate an agent’s planning or goal-setting processes. By subtly influencing reflection steps or intermediate objectives, adversaries can redirect the agent toward malicious outcomes while it still believes it is following its mission.

7. Misaligned and deceptive behaviors

Misaligned and deceptive behaviors occur when agents intentionally or unintentionally take harmful actions to achieve objectives. These behaviors often arise from faulty reasoning, reward hacking, or adversarial manipulation of objectives.

Beyond these core threats, OWASP also highlights additional risks that enterprises must monitor:

• Repudiation and untraceability (T8): Insufficient logging and transparency make it difficult to trace agent actions or enforce accountability.

• Identity spoofing and impersonation (T9): Attackers mimic agents or users to execute unauthorized actions.

• Overwhelming human-in-the-loop (T10): Adversaries flood human reviewers with alerts or tasks to exploit cognitive overload.

• Unexpected RCE and code attacks (T11): Malicious code is injected or executed through AI-generated scripts and environments.

• Agent communication poisoning (T12): Attackers manipulate inter-agent channels to spread misinformation or disrupt workflows.

• Rogue agents in multi-agent systems (T13): Compromised or malicious agents operate outside monitoring boundaries.

• Human attacks on multi-agent systems (T14): Adversaries exploit delegation and trust relationships between agents.

• Human manipulation (T15): Direct human-agent interactions are abused to mislead users, spread misinformation, or coerce action.

Together, these emerging risks illustrate that securing agentic AI requires protection not just at the model level, but across memory, tools, communication, and human oversight.

Agentic AI security frameworks and emerging standards

The rapid adoption of agentic AI systems has forced regulators, standards bodies, and industry groups to rethink governance. The result is a growing push to formalize AI security frameworks and AI agent governance standards tailored to autonomous systems. 

Unlike traditional AI, agentic models evolve after deployment, chain together tools, and operate across organizational boundaries, which makes static, one-time certifications insufficient.

Below are the leading frameworks and guidelines shaping the future of agentic AI or autonomous AI security:

OWASP agentic AI security guidelines

The OWASP ASI has become one of the most influential resources for practitioners. Building on its “Top 10” series, OWASP has published a taxonomy of 15 threat categories for agentic AI, ranging from memory poisoning to human manipulation. These guidelines extend beyond prompt injection to cover unique attack vectors such as tool misuse, non-human identities (NHI), and inter-agent communication poisoning.

NIST AI risk management framework

Released in 2023, NIST’s AI Risk Management Framework (AI RMF) is a voluntary guideline that provides a lifecycle-based approach to identifying, assessing, and mitigating AI risks. It emphasizes governance structures, quantitative and qualitative risk assessments, and continuous monitoring. 

ISO/IEC standards for AI security and governance

ISO has accelerated its work on AI governance with new standards directly relevant to agentic AI:

• ISO/IEC 42001:2023 – AI management systems: The first global AI governance standard, focusing on organizational structures for risk, transparency, and accountability.

• ISO/IEC 23894:2023 – Guidance on risk management: Outlines how organizations can identify, assess, and manage AI-specific risks, integrating risk management across the AI lifecycle.

• ISO/IEC TR 24027:2021 – Bias in AI systems and AI aided decision making: Provides methods for assessing and addressing bias-related vulnerabilities in data collection, training, design, testing, evaluation, and use.

While these were not designed specifically for autonomous agents, they are being extended to cover agentic use cases, often by layering stricter HITL (human-in-the-loop) oversight and logging requirements.

Cloud security alliance (CSA) recommendations

The Cloud Security Alliance's AI Controls Matrix (AICM) is a vendor‑agnostic framework offering 243 control objectives across 18 security domains, designed to help enterprises govern AI systems with trust, transparency, and accountability. For adopting agentic AI, it offers critical guidance on identity and access management, model security, supply chain oversight, and governance alignment.

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Essential agentic AI security controls and best practices

While frameworks might provide structure, enterprises still need concrete controls. These agentic AI best practices are the most critical today:

1. Agent authentication and authorization

Every AI agent must have a verifiable identity, just like a human user. Strong cryptographic credentials, combined with role-based or attribute-based access controls, ensure agents operate only within approved boundaries. This prevents unauthorized agents or impersonators from entering sensitive workflows.

2. Runtime monitoring and anomaly detection

Since agents operate continuously, real-time monitoring of their actions is critical. Behavioral baselines can help detect unusual activity, such as sudden spikes in tool usage or abnormal data access patterns. Integrating these signals into SIEM platforms enables faster detection and response.

3. Tool access controls and sandboxing

Agents often rely on APIs, plugins, and enterprise tools to complete tasks. Least-privilege permissions and sandboxed execution environments prevent them from abusing integrations or chaining tools in harmful ways. For high-risk actions, additional approval workflows or policy gates should be enforced.

4. Memory integrity protection

Persistent memory is both a capability and a liability. Validation of data written to memory, cryptographic checks, and isolation between sessions can prevent poisoning attacks. Regular memory sanitization and rollback features provide a failsafe when anomalies are detected.

5. Input validation and sanitization

Inputs to an agent should never be assumed safe. Sanitizing text, code, and structured data before processing reduces the risk of prompt injection and malicious payloads. Validation frameworks should be applied consistently across both user inputs and external content fetched by the agent.

6. Output filtering and verification

Before an agent’s outputs are executed or shared, they should be checked against predefined safety and policy rules. Output verification can detect attempts to exfiltrate sensitive data, generate harmful instructions, or execute unauthorized tool calls. 

7. Agent behavior constraints and guardrails

Defining strict boundaries for agent actions reduces the risk of unintended autonomy. Guardrails can be implemented as policy engines that block disallowed behaviors, cap resource usage, or require human approval for sensitive tasks. This ensures agents act consistently with business and security requirements.

8. Audit logging and forensics

All agent actions, from decisions to tool calls, must be logged in tamper-resistant systems. Cryptographically signed logs allow forensic analysis and compliance reporting in the event of an incident. Transparent logging also supports explainability, helping teams understand why an agent acted a certain way.

9. Secure agent-to-agent communication

When multiple agents collaborate, their communications become a new attack surface. Encryption, authentication, and message validation ensure one compromised agent cannot spread malicious instructions across a network.

10. Emergency stop and override mechanisms

Every deployment should include a reliable way to pause or shut down agents immediately. These kill switches act as the last line of defense if an agent behaves unexpectedly or is compromised. Regular testing of these mechanisms ensures they will function during real incidents.

How to implement agentic AI security at your organization

Agentic AI raises the stakes because security must be designed into governance, policies, and daily operations from the start. The following steps outline how enterprises can move from principles to execution:

Establish AI governance committees

Form a cross-functional body that brings together security, IT, data, and business leaders. This committee sets oversight structures, defines acceptable use boundaries, and ensures human accountability remains in place for autonomous agents. Regular reviews allow the committee to adapt controls as agents evolve and take on new responsibilities.

Apply structured risk assessment methodologies

Adopt structured frameworks such as the CSA trait-based model or NIST AI RMF to analyze risks systematically. This is the foundation of effective agentic AI risk management. Assess not only technical vulnerabilities but also risks created by persistence, planning, and tool usage. Risk evaluations should be revisited frequently to capture new behaviors that emerge as agents self-adapt.

Enforce policies through guardrails and automation

Move policies beyond static documents and turn them into enforceable rules. Automated guardrails, policy engines, and runtime checks can restrict agent behavior and prevent unsafe actions. Escalation paths should also be clearly defined so high-risk operations receive human approval before execution.

Manage vendors and third-party risk proactively

Most agentic AI depends on external APIs, plugins, and cloud platforms. Enterprises should ensure contracts and due diligence processes extend security requirements to these third-party tools. This includes auditability, kill switches, and red-team testing to validate vendor resilience.

Audit continuously and verify compliance in practice

Audits must verify not only models or code but also how agents behave in live environments. Continuous compliance monitoring provides assurance that policies are enforced at runtime. Scenario-based red-teaming should complement audits to test defenses under realistic attack conditions.

To make these steps actionable, Rippling offers the agentic AI security toolkit, authored by Nate Lee, co-author of the Cloud Security Alliance’s Secure Agentic System Design: A Trait-Based Approach. The toolkit translates complex frameworks into practical steps, guiding teams through risk mapping, mitigation design, and integration into broader IT strategy.

Streamline agentic AI security across your enterprise with Rippling

As enterprises experiment with autonomous AI agents, the fundamentals of IT security matter more than ever. Identity, devices, and access controls remain the first line of defense, and they’re exactly where Rippling IT management software provides strength. 

Rather than bolting on new tools, Rippling helps organizations build a secure foundation that naturally extends to agentic AI deployments:

• Unified identity management for humans and agents: Rippling centralizes identity management across your workforce and connected systems. This means you can manage both human users and AI agents with the same lifecycle policies, from automated provisioning to role-based access controls, ensuring consistent governance without gaps.

• Comprehensive device security and compliance monitoring: AI agents operate through the same devices, servers, and cloud systems your teams already use. Rippling gives you real-time visibility into those environments, enforcing compliance baselines and detecting anomalies that could signal misuse or compromise.

• Centralized access and real-time inventory: With Rippling, enterprises get a single point of control for access permissions and system inventory. You’ll know exactly which resources are in use, what agents or users can reach them, and where risks may emerge, all tracked and enforced automatically

• Seamless integration with your security stack: Rippling doesn’t replace your existing defenses. Instead, it connects with SIEM platforms, policy engines, and other security tools, providing unified visibility across the IT and AI layers of your business. The result is a resilient, integrated security posture that keeps pace with both traditional risks and new AI-driven workflows.

By anchoring AI agent security in the fundamentals of identity, devices, and access, Rippling helps enterprises scale safely without overcomplicating their security stack.

FAQs on agentic AI security

What makes agentic AI security different from traditional AI security?

Traditional AI security protects stateless systems that respond to prompts, while agentic AI security addresses autonomous systems that maintain memory, make independent decisions, and access multiple business systems. The key differences include securing persistent memory stores, monitoring autonomous decision-making, and controlling agent-to-agent communications.

How do you detect when an AI agent has been compromised?

Look for behavioral deviations like unusual data access patterns, unexpected tool usage, or communications with unauthorized systems. Real-time monitoring with anomaly detection can identify suspicious activities, though memory poisoning attacks are particularly difficult to spot because they gradually alter behavior over time.

What are the biggest risks of deploying agentic AI in enterprise environments?

The top three risks according to the OWASP ASI, are memory poisoning (attackers manipulating agent memory), tool misuse (agents being tricked into abusing system access), and privilege compromise (agents exploited to escalate access). Multi-agent environments face additional risks of cascade failures where one compromised agent affects others.

How should organizations prepare for agentic AI security challenges?

Start by establishing governance frameworks with cross-functional committees and clear policies for agent deployment. Invest in monitoring tools designed for agentic systems and train security teams on agent-specific threats. Begin with low-risk use cases to build expertise before deploying agents in critical processes.

What compliance requirements apply to agentic AI systems?

Agentic AI systems are not exempt from existing rules such as the GDPR, which already governs how personal data is collected and processed. Their autonomy, however, raises new challenges around transparency, purpose limitation, and accountability. In Europe, the AI Act takes a risk-based approach that may classify certain agentic AI deployments as “high risk” or even prohibited, depending on context. 

How can you prevent AI agents from accessing unauthorized resources?

Implement least-privilege access controls and role-based access management that limits agents to necessary resources only. Use API gateways that evaluate agent requests against policy in real-time. Regular access reviews and automated privilege management prevent agents from accumulating excessive access over time.

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This blog is based on information available to Rippling as of September 4, 2025.