Users: the Often Overlooked Factor in AI Security

No matter how robust your models are or how well your data is governed, the way people use AI inside your organization can expose you to significant risk. From accidental misuse by well-meaning employees to deliberate manipulation by external actors, the human factor introduces a broad, often overlooked risk surface in real-world AI deployments. A detailed AI usage policy that fits your organization is a necessity if you want to mitigate the critical risks. 

These usage risks often emerge when AI systems are integrated into workflows without sufficient guardrails, clear access policies, or oversight mechanisms. Giving AI agents too much autonomy, especially when they interact with sensitive systems or data, can compound these issues. The dangers are especially acute in natural language interfaces, where unpredictable or adversarial inputs can lead to erratic behavior, compliance violations, or outright security breaches. 

Understanding these vulnerabilities is essential not just for AI security and governance, but for overall organizational resilience. The following points explore the key ways in which user behavior intersects with technical implementation, and where things are most likely to go wrong. 

Lack of Input Guardrails 

When users, internal teams or external customers, interact with AI systems, especially conversational interfaces, they often do so in unpredictable ways. Without strong input validation or filtering, freeform prompts can lead to degraded performance, misinterpretations, or unintended actions. This leads to lost time, frustrated users, eroded trust, and in critical use cases, unexpected system behavior when AI acts autonomously. 

To address this, companies should implement proper input guardrails. 

AI systems are only as safe as the inputs they receive. Without robust input filter and validation layers, conversational interfaces, and even agent-based apps, can be manipulated, tricked, or misled.  

There are numerous examples of malicious use of publicly available chatbots. Like the one, where users forced the chatbot to critique the company it was serving. Or a different example, where a chatbot started to swear and use slang words in its responses, because this was aligned with the “personality” it was supposed to follow.   

Whether through adversarial attacks, careless user phrasing, or malicious actors, unchecked inputs risk performance degradation, faulty decisions, or outright system compromise. Input guardrails are therefore foundational to any trustworthy AI deployment. 

Lack of Output Guardrails 

AI systems have advanced way beyond just generating content, they increasingly make decisions, trigger workflows, or initiate downstream actions (even modifying data). Without robust output guardrails, these outputs can lead to serious unintended consequences. Whether it’s a chatbot issuing a misleading policy recommendation, or an AI agent triggering system-level changes, insufficient oversight can result in behavior that’s operationally disruptive, financially costly, or legally risky. 

One major risk is execution without verification: when AI-generated outputs are acted on automatically, without sufficient human review or validation. For example, a support bot integrated into a CRM might close tickets or initiate refunds based on ambiguous customer messages. While this automation can improve efficiency, it can also create opportunities for abuse or costly errors if not bounded by business logic or human oversight. 

Output guardrails should include: 

• Action confirmation layers, where AI suggestions require approval before execution. 

• Scope limitations, such as restricting what systems the AI can touch or what functions it can call and in what situations. 

• Syntax and semantic checks of generated SQL queries to validate if generated data manipulation instructions are not harmful for the company’s database.  

• Response filters and toxicity checks to block misleading, noncompliant, or high-risk suggestions. 

• Logging and audit trails for all AI-generated actions, so organizations can track decisions and assign accountability. 

Autonomous Agent Misuse 

AI agents that are integrated with critical systems, such as email, file systems, browsers, APIs, or cloud infrastructure, pose substantial risk when operating without proper oversight. Left unchecked, they may access sensitive data, dispatch unauthorized communications, or carry out external actions driven by misinterpreted or malicious prompts. Robust governance and human-in-the-loop mechanisms are essential. 

Prompt Injection Attacks 

Prompt injection is currently viewed as one of the most pressing threats in AI applications. Attackers may craft inputs that bypass safety filters, leak internal data, or coerce the model into performing tasks outside its intended scope. This can compromise internal logic, expose proprietary information, or produce unintended outputs. 

Denial-of-Service (DoS) and Abuse Attacks 

AI applications that invoke costly model inference or trigger downstream services are susceptible to cost-based DoS attacks, where repeated queries drive up usage bills or degrade application performance. Rate limiting, user quotas, and usage monitoring are critical mitigations. 

Model Theft via Usage Interfaces 

Similar to risks at the model level, usage patterns can enable knowledge extraction or behavioral cloning through high-frequency interaction and analysis of responses. This can compromise proprietary value or intellectual property embedded in customized models. 

Over-Reliance and Blind Trust 

Users may overestimate the reliability of AI outputs, especially in enterprise settings where confidence and speed are often mistaken for correctness. This leads to human-in-the-loop erosion, where critical decisions are automated without sufficient verification. 

Roadmap for Usage Risks Mitigation 

Addressing usage-related risks starts with recognizing that how AI systems are used can be just as impactful as how they’re designed and developed. Even the most secure architectures and carefully curated data pipelines can be compromised by careless interactions, excessive permissions, or weak governance. Usage is where AI leaves the lab and enters the real world; where human decisions, business pressures, and complex environments test its integrity. This layer requires a strong AI tool usage policy; one that will enforce continuous oversight, clear accountability, and human-in-the-loop mechanisms to maintain trust and control. 

To minimize these risks, organizations need to implement guardrails that blend technical safeguards with procedural discipline. This means validating inputs and outputs, stress-testing AI performance in realistic scenarios, managing permissions tightly, and assigning role-specific controls over access and functionality. Equally important is ensuring that teams understand the proper use and inherent limitations of AI tools, preventing misuse, overreliance, or unintended outcomes. 

The recommendations below outline actionable steps organizations can take to promote responsible, secure, and resilient AI use, ensuring that innovation and safety advance hand in hand. 

Implement Use Case–Specific Input and Output Guardrails 

Most cloud-hosted models provide generic safety filters: blocking offensive language, other prompt injection attacks, or limiting abuse of API calls. These solutions are not yet sufficient for enterprise deployments. You must analyze your specific use case to determine what additional input and output constraints are required. This can include: 

• Regex-based filtering for dangerous or malformed input 

• Custom classifiers for content or intent (e.g., filtering legal advice, financial claims, confidential identifiers) 

• Prompt segmentation, where prompts are composed using only whitelisted components 

• Rate limiting and cost monitoring, especially for tools with per-token pricing 

Use Feedback Loops and Human-in-the-Loop Oversight 

No guardrail system is complete from day one. Incorporate stress testing and real-world user feedback to identify edge cases and failure modes. Whether you’re adding documents to a retrieval-augmented generation (RAG) pipeline, fine-tuning models, or refining prompt strategies, human supervision is crucial. Over time, policy and filtering mechanisms should evolve based on observed behavior. 

Limit AI Agent Permissions: Avoid Excessive Agency 

When AI agents can interact with tools (e.g., databases, email, browsers), apply the principle of least privilege. Do not grant agents access to data and/or §systems they do not need. Excessive agency, when agents are given too much control, creates disproportionate risk. Ensure fine-grained permissioning, clear task boundaries, and user confirmation for any high-impact action. 

Separate Prompts from Content 

Designing applications where user input can be mixed with internal prompts (e.g., system or task instructions) opens the door to prompt injection attacks. Always segregate user content from application logic, using system prompts and structured inputs instead of natural language chaining wherever possible. 

Manage User Expectations and Avoid Overreliance 

AI systems, especially LLMs, may appear competent across a wide range of tasks, but they’re not universally reliable. Educate users about strengths and limitations and discourage overreliance in critical areas. For example, LLMs are not optimal for statistical or predictive analysis: for such tasks, structured machine learning models are far more reliable and auditable. 

Establish Detection and Response Mechanisms 

As usage scales, so does the potential for misuse, failure, or attack. Implement a detection and response framework, analogous to cybersecurity protocols, including: 

• Prompt logging and anomaly detection 

• Access and usage pattern monitoring 

• Failover or circuit-breaker systems in case of runaway costs or abuse 

• Incident response playbooks for handling data leaks, inappropriate outputs, or unexpected system actions 

Addressing User Risks for AI Security 

Even the most technically sound AI systems can be undermined by how people use them. From weak input validation to overreliance on automated outputs, human interaction remains one of the biggest and least predictable risk factors in enterprise AI. Building resilience means combining strong technical guardrails with governance, oversight, and user education to ensure AI operates safely in real-world contexts. 

To explore a broader, more comprehensive approach to AI safety beyond user-related risks, read our compendium on navigating safe AI deployments, which outlines best practices for secure, responsible, and resilient AI implementation across the enterprise.