Securing Agentic AI: A CTO's Guide to Top Risks and Mitigation

Executive Summary

Generative AI systems, particularly those powered by Large Language Models (LLMs) used in agentic systems, introduce a new frontier of critical security and safety risks. As enterprises deploy these powerful tools, technology leaders must move beyond viewing security as a feature and embed it into the entire AI lifecycle. This guide provides a comprehensive breakdown of the most critical risks—from rogue agent actions and prompt injection to data poisoning and sensitive data disclosure. We will detail actionable mitigation strategies and clarify the shared responsibility between the Model Creator (the entity building the foundational model) and the Model Consumer (the enterprise deploying the agent) in securing the AI ecosystem.

1. Risks Related to Agent Autonomy and Unintended Actions

The probabilistic nature of AI agents and their ability to execute actions in the real world introduce high-impact security risks that demand a multi-layered defense.

Rogue Actions (RA)

Description: This risk involves unintended actions executed by a model-based agent, whether accidental (due to misalignment or reasoning mistakes) or malicious (caused by prompt injection or data poisoning). The severity of a rogue action is directly proportional to the agent's capabilities and system permissions.
Primary Responsibility: Model Consumers.
Mitigation Strategies:
- Input Filtering and Standardization: Filter and standardize all inputs before they reach the model to neutralize malicious payloads.
- Tool Limitations: Strictly define the agent's permissible actions and toolset within its system instructions.
- Least-Privilege Permissions: Govern the agent’s capabilities using policy engines and time-bound, scoped credentials for tool access.
- Adversarial Training: Work with the model creator to harden the model's reasoning core against common adversarial attacks like prompt injection.
- User Control & Output Sanitization: Sanitize all model outputs before rendering and implement application-level safeguards, such as user confirmation prompts for critical actions.

Prompt Injection (PIJ)

Description: This is the act of causing a model to execute commands "injected" into a prompt, exploiting the blurry boundary between instructions and input data. This includes "jailbreaks" that trick the model into generating unsafe or restricted content.
Shared Responsibility: Model Creators and Model Consumers.
Mitigation Strategies:
- Input/Output Validation: Implement rigorous screening and filtering of both user inputs and model-generated outputs.
- Adversarial Training: The model creator must conduct extensive training, tuning, and evaluation to fortify the model against injection techniques.

Insecure Integrated Component (IIC)

Description: This involves exploiting vulnerabilities in third-party software (like plugins, libraries, or APIs) that interact with the AI model. An attacker could gain unauthorized access, introduce malicious code, or compromise connected systems.
Primary Responsibility: Model Consumers.
Mitigation Strategies:
- Strict Agent Permissions: Enforce stringent, least-privilege permissions for all agents and the plugins they can access.
- Application Hardening: Regularly scan for and patch vulnerabilities within all application components and dependencies that interact with the agent.

2. Risks Related to Data Privacy and Confidentiality

Generative AI systems pose a significant risk of leaking private or proprietary information, both from their training data and from the data they access at runtime.

Sensitive Data Disclosure (SDD)

Description: The unintentional disclosure of private or confidential information through model querying. This risk is amplified in agentic systems, which may have privileged access to sensitive user data (emails, files, credentials) via integrated tools.
Shared Responsibility: Model Creators and Model Consumers.
Mitigation Strategies:
- Data Minimization & Filtering: Creators must remove or label sensitive data during sourcing and processing before model training.
- Output Filtering: Both creators and consumers must filter model outputs to prevent the disclosure of sensitive patterns.
- Rigorous Testing: Creators must test models for potential data leakage vulnerabilities.
- Agent Access Controls: Consumers must enforce strict permissions on the agent’s access to tools and data repositories.
- User Confirmation: Consumers should implement application-level warnings to get user confirmation before executing actions that may involve sensitive data.

Excessive Data Handling (EDH)

Description: The collection, retention, processing, or sharing of user data beyond what is legally or ethically permissible, creating significant legal and policy challenges.
Primary Responsibility: Model Creators.
Mitigation Strategies:
- Proactive Data Management: Implement robust data filtering, processing, archiving, and deletion policies.
- Automated Governance: Use automation to alert for or delete models trained with outdated or non-compliant data.

3. Risks Related to Model Integrity and Trustworthiness

These risks involve the unauthorized tampering or malicious modification of the model, data, or code that underpins the AI system.

Data Poisoning (DP)

Description: The act of maliciously modifying data sources used during training or retraining to degrade model performance, skew results, or install hidden backdoors.
Primary Responsibility: Model Creators.
Mitigation Strategies:
- Training Data Sanitization: Sanitize and verify all data before it is ingested for training.
- Integrity Management: Implement cryptographic mechanisms to ensure data and model integrity throughout the lifecycle.
- Strict Access Control: Employ secure systems and strict access controls for all data and model artifacts.

Model Source Tampering (MST)

Description: Tampering with a model’s source code, dependencies, or weights, often through supply chain or insider attacks. This can introduce vulnerabilities or hidden backdoors.
Primary Responsibility: Model Creators.
Mitigation Strategies:
- Code & Weight Integrity Management: Employ robust integrity checks for all model code and weights.
- Secure ML Tooling: Use secure-by-default infrastructure and MLOps tools.
- Access Controls & Inventory: Implement robust access controls and comprehensive inventory tracking for all model assets.

Model Evasion (MEV)

Description: Causing a model to produce incorrect or harmful inferences by slightly perturbing the prompt input (known as adversarial examples).
Shared Responsibility: Model Creators and Model Consumers.
Mitigation Strategies:
- Adversarial Training and Testing: Creators must develop robust models using extensive and diverse datasets to make them resilient to such attacks. Consumers should contribute to this by reporting observed evasions.

Foundational Mitigation Controls for the Enterprise

Across all risks, several foundational controls are non-negotiable for any enterprise deploying agentic AI systems, as defined by a modern Agent Development Lifecycle (ADLC):

Sandboxing: This is a paramount security control. Agents and the tools they use must run inside constrained execution environments to enforce least-privilege access and prevent a compromised agent from causing widespread damage.
Identity and Access Management (IAM): Every agent must be issued a unique, traceable identity. This ensures that every action is auditable, providing the verifiable data trail required for accountability and regulatory compliance.
Deep Observability and Tracing: Monitoring must go beyond simple technical metrics. It must capture and analyze agentic reasoning traces, tool usage logs, and behavioral metrics like hallucination rates and behavioral drift. This is essential for root-cause analysis and meeting audit requirements.
Gateway Governance: Implementing an API gateway for agents allows for centralized control. This is where you enforce policies like rate limiting, throttling, and outbound access controls, creating a critical chokepoint for managing agent behavior at scale.
Output Validation and Sanitization: Never trust model output implicitly. All responses must be validated and sanitized before being passed to users or downstream systems to mitigate risks like insecure code generation or data disclosure.

By adopting a security-first mindset and implementing these multi-layered controls, technology leaders can confidently deploy generative AI, transforming it from a potential liability into a secure, powerful engine for enterprise innovation.