Most conversations about AI governance still treat large language models (LLMs) as if they are simply chatbots—tools that only answer questions or generate text. However, the reality is quite different. The AI tools your teams are piloting today, especially agentic LLMs, can do much more than just respond to prompts. They can send emails, close support tickets, move money, edit records, and even push code. These systems are now being integrated into critical ai applications across organizations, requiring robust governance to ensure responsible deployment. This shift from passive assistance to active task execution introduces new risks that organizations must manage carefully.

The critical moment when an LLM stops merely suggesting actions and starts acting as the user is what we call a user-assumption event. If your AI governance framework does not detect and control these events, then your entire AI policy risks being little more than a polite suggestion without enforceable controls. Senior leadership is ultimately responsible for ensuring these frameworks are effective and enforced. This article focuses on how to spot user-assumption events early and implement humane, explainable guardrails to ensure safe and compliant use of agentic LLMs.

What Is an Agentic LLM?

An agentic LLM is a type of artificial intelligence that operates autonomously or semi-autonomously by interacting with other tools, systems, APIs, or external services to complete tasks on behalf of users. Unlike traditional AI models that only generate text based on prompts, agentic LLMs can:

• Call external tools or APIs, such as sending emails, filing tickets, querying databases, or integrating with cloud-based services.
• Execute multi-step processes aimed at achieving specific goals, for example, “triage these alerts” or “renew these contracts.”
• Operate on their own schedule or triggers, rather than waiting for direct human input.
• Access and synthesize data from multiple sources, such as databases, APIs, and web search, to improve the accuracy and comprehensiveness of their outputs.
• Retrieve and incorporate search results into their workflows to ensure information is current and relevant.
• Execute python code to analyze data, automate tasks, or generate reports as part of complex research or operational workflows.

In essence, agentic LLMs shift the AI’s role from “answer this question” to “go do this for me.” This transition is not inherently negative. For example, a security analyst using an LLM-powered agent to pull logs, enrich threat indicators, and draft an incident report is a valuable application of AI technologies. These capabilities are developed through extensive training on large datasets (training data), including instruction tuning and reinforcement learning, which enable the models to acquire goal-directed behaviors and integrate external tools. Agentic LLMs leverage machine learning algorithms to perform complex, multi-step tasks and adapt to evolving requirements.

However, risks arise when:

• The agent acts with the same privileges as the human user, effectively inheriting their access rights.
• The agent can make changes without a clear human checkpoint or approval.
• Logs and user interfaces make it appear as though the human user performed all actions personally, obscuring accountability.

Understanding these characteristics of agentic AI is essential for designing effective AI governance frameworks that balance innovation with security.

What Is a User-Assumption Event?

A user-assumption event occurs whenever an agentic LLM performs an action in a system in a way that would normally be attributed to the human user. In other words, the AI acts as if it were the user, using their credentials or identity to execute tasks. This phenomenon can undermine accountability and create significant AI security risks.

Examples of user-assumption events include:

• An LLM sending an email from a corporate mailbox under the user’s name, rather than from a designated bot address.
• The LLM clicking “approve invoice” or “ship order” inside an enterprise resource planning (ERP) system using the user’s access token.
• Closing a JIRA or ServiceNow ticket under the user’s username.
• Editing a policy document and routing it for signatures as if the human user performed these actions.

If audit trails indicate that you performed an action you never saw or authorized, it means governance has already failed. Most user-assumption events are not malicious; they often result from:

• Misconfigured AI tools, such as “auto-send” features turned on by default.
• Overly broad API scopes that grant full mailbox access when “draft-only” would suffice.
• Side projects or scripts that quietly evolve from assistants into autonomous actors without updated policies or oversight.

Despite the lack of malicious intent, these events erode accountability and increase organizational risk, making it critical to detect and manage them effectively. Unmanaged user-assumption events can have a negative impact on organizational security and compliance, potentially leading to harmful outcomes and regulatory violations.

To address these challenges, organizations should adopt a risk based approach to detect and manage user-assumption events, ensuring effective mitigation and regulatory compliance.

Why AI Security, Privacy, and Legal Should Care

Identity and Least Privilege Get Fuzzy

Traditional security controls rely on clearly identifying who performed an action—distinguishing between users and service accounts, each with specific roles and approval workflows. When agentic LLMs act as users:

• Least privilege principles on paper can become effectively max privilege by delegation, as the AI inherits broad access rights.
• Human approvals intended to be deliberate become rubber-stamped by automated agents.
• Forensic investigations become guesswork, as it’s unclear whether actions were performed by the person or their AI agent.

This blurring of identity complicates risk management and incident response.

Regulatory Pressure on Human Oversight

Regulators across industries are increasingly focused on ensuring human oversight of consequential decisions, especially in domains like employment, finance, healthcare, and data access. Key regulatory expectations include:

• Transparent human oversight of AI-driven decisions.
• Explainability—being able to reconstruct how a decision was made, including AI involvement.
• Clear assignment of responsibility for actions taken.

If an agentic LLM silently acts as a user in these contexts, your compliance story becomes fragile, exposing your organization to legal and reputational risks.

Incident Response Gets Harder

When incidents occur, organizations need to determine:

• Was this caused by prompt injection attacks (e.g., “approve everything from now on”)?
• A misconfigured integration with excessive privileges?
• A human misusing AI tools?

Without clear visibility into user-assumption events, answers become uncertain, delaying response and increasing the likelihood of costly mistakes.

---

Where User-Assumption Events Hide

User-assumption events can manifest in various places within your AI ecosystem. Early detection requires understanding where these risks typically emerge.

Maintaining a curated list of approved AI tools and automations, vetted by security and legal teams, is essential to reduce the risk of unauthorized user-assumption events.

Productivity Suites

Agentic LLMs integrated with productivity tools can:

• Send draft emails automatically.
• Auto-reply to routine customer inquiries.
• Accept calendar invites based on predefined criteria.

If the LLM sends emails directly or accepts invites without human intervention, it acts as the user. Conversely, if the LLM only drafts content and the human presses “send,” the user remains clearly in control.

Internal Copilots on Top of Business Apps

Many enterprises deploy AI copilots that interact with business-critical applications:

• Security copilots that can close alerts or isolate endpoints.
• Finance agents that create or modify invoices.
• HR assistants that update employee records.

When these agents use user tokens rather than constrained service accounts, every action appears as if performed by the human user, complicating audit trails.

Custom Scripts and Automations

Small scripts or chatbots that chain LLM calls with internal APIs often bypass formal governance reviews. These “tiny tools” can quickly evolve from helpers into autonomous actors operating under user identities, increasing AI adoption risks.

---

Minimizing Assumptions in AI

Minimizing assumptions is a foundational step in building trustworthy and effective AI systems. Every assumption—about user needs, data quality, or how systems operate—can introduce hidden biases into AI models, leading to inaccurate outputs and unintended consequences. To address this, organizations must prioritize robust user research and actively seek user feedback throughout the AI development lifecycle.

Engaging with a diverse range of users helps teams identify and challenge their own assumptions, ensuring that AI systems are designed to serve real-world needs rather than hypothetical scenarios. Techniques like A/B testing and controlled experimentation allow teams to validate their assumptions in practice, revealing gaps between expected and actual user behavior. This iterative process is essential for refining models and improving system performance.

By embedding user research and feedback loops into the development process, organizations can identify critical blind spots early, adapt their AI models to better reflect user realities, and ultimately create AI systems that are more accurate, efficient, and aligned with business goals. In a landscape where AI adoption is accelerating, minimizing assumptions is not just good practice—it’s essential for building AI solutions that users trust and rely on.

---

How to Detect User-Assumption Events

Detecting user-assumption events does not require exotic technology but rather a structured approach using specific signals and conventions.

Agentic LLMs are increasingly used for tasks like competitive analysis and market research, both of which involve handling sensitive data and require careful monitoring for user-assumption events.

1. Separate Human and Agent Identities

Wherever possible, maintain distinct identities for humans and AI agents:

• Use separate accounts for agents, such as service accounts, bot users, or explicit “assistant” aliases.
• Add metadata to logs indicating the actor type (e.g., user vs agent) to enable easy filtering.
• Encourage developers to include headers or flags in API calls initiated by LLMs.

This way, answering “Who did this?” becomes a straightforward query rather than guesswork.

2. Hunt for “Too Perfect” Behavior in Logs

Look for patterns in logs that suggest automated agent activity:

• Bursts of very similar actions executed in rapid succession (e.g., dozens of approvals in a minute).
• Actions consistently occurring outside the user’s typical working hours.
• High-volume edits or approvals without corresponding user interface sessions or keystrokes.

These anomalies often reveal agents quietly acting as users.

3. Watch the Browser for “Just Do It for Me” Moments

Even when agents operate in the backend, the initial delegation often happens in the browser:

• Users enabling “auto-send,” “auto-approve,” or “never ask me again” options within AI tools.
• Prompts instructing the AI to “handle all these requests automatically” in chat interfaces.

Browser extensions can detect when users grant broad, open-ended powers to LLMs, trigger context-aware warnings, and feed these events into governance processes before they become standard practice.

4. Log User-Assumption Events as First-Class Data

Explicitly log suspected user-assumption events with structured data, including timestamps, policy IDs, decision outcomes, roles, resource tags, and compliance framework mappings. Over time, this data provides:

• Frequency and distribution of user-assumption events.
• Identification of teams most reliant on full delegation.
• A baseline for security, privacy, and legal teams to review and present to auditors.

---

Designing Guardrails Without Killing the Good Stuff

Agentic LLMs bring significant efficiency and innovation benefits. The goal is not to ban them but to shape how they are used through thoughtful guardrails. When designing these guardrails, it is essential to prioritize accuracy, especially in high-stakes workflows where correctness and precision are critical to user outcomes.

Observe → Warn → Narrow Block

1. Observe
   Begin by detecting and logging potential user-assumption events without blocking. This phase helps you understand how users interact with agentic AI and identify emerging risks.
2. Warn
   Introduce human-readable warnings when users approach risky delegations, for example:

   • “You’re about to let this AI assistant approve changes under your name. This affects audit trails and responsibility assignment. Choose a limited mode or confirm you understand.”

   • The tone should be calm, specific, and non-shaming to encourage informed decision-making.
3. Narrow Block
   Reserve hard blocks for clearly high-risk actions, such as:
   • Provide safer alternatives like draft-only modes, supervisor approvals, or tighter API scopes to maintain efficiency while managing risk.
   • Auto-approving large financial transactions.
   • Automatically changing access rights or security settings.
   • Editing legal, HR, or policy documents without human review.

---

Best Practices for AI Development

Developing secure, reliable, and effective AI systems requires a holistic approach that combines technical excellence, operational discipline, and strong governance. At the technical level, it’s essential to train AI models on high-quality, diverse data sets to reduce bias and ensure robust performance. Data protection and integrity must be prioritized to safeguard sensitive information and maintain trust in how AI systems operate.

Operationally, automated monitoring and continuous testing are critical for identifying and addressing issues before they escalate into costly mistakes. Proactive monitoring helps teams quickly detect anomalies, maintain data integrity, and ensure that AI models continue to deliver accurate results as conditions change.

Governance frameworks play a pivotal role in aligning AI development with organizational values and regulatory requirements. Establishing clear roles and responsibilities—such as appointing a chief AI officer—ensures accountability and oversight across the AI lifecycle. These frameworks help organizations identify and manage risks, set standards for ethical AI use, and create audit trails that support compliance and transparency.

By integrating these best practices—rigorous data management, automated monitoring, and strong governance—organizations can develop AI systems that are not only technically sound but also secure, compliant, and ready to deliver value at scale.

A 90-Day Plan to Get Ahead of Agentic Risk

To proactively manage agentic LLM risks, organizations can follow a structured 90-day plan. Throughout this plan, it is crucial to test assumptions at each stage to ensure that policies and controls are effective and aligned with real-world usage.

By the end of the 90 days, organizations should have a clear understanding of their agentic risk landscape and a set of enforceable controls. Drawing on past experiences with AI deployments can further inform risk management strategies, helping to avoid repeating mistakes and strengthening overall governance.

Days 0–30: Name It and Find It

• Define agentic LLMs and user-assumption events clearly in your AI acceptable-use policy.
• Collaborate with development and platform teams to inventory where LLMs can already call tools or APIs.
• Conduct a basic log review to identify “too perfect” behavior patterns, such as bursts of actions or off-hours activity.

Days 31–60: Tag and Observe

• Implement conventions for marking agent versus human actions in logs.
• Require distinct identities (service accounts or bot users) for new agentic projects.
• Use browser-level monitoring to flag “auto-everything” prompts and settings, logging these events without blocking.

Days 61–90: Warn and Refine

• Develop a set of warning messages for risky delegations in sensitive systems.
• Apply the Observe → Warn → Narrow Block approach to one or two high-impact workflows, such as financial approvals or access changes.
• Begin tracking user-assumption events in structured logs (e.g., CSV files) for quarterly review by security, privacy, and legal teams.

By the end of this period, your organization will have established a vocabulary, telemetry, and governance path that transforms accidental agentic risks into manageable, explainable processes.

---

Where Govnr Quietly Fits In

As enterprises increasingly experiment with agentic LLMs, much of the real risk emerges in the browser—the “first mile” where users paste data, enable “auto” settings, or grant broad powers to AI assistants. Govnr (govnr.ai) addresses this critical phase of AI governance by providing:

• A Policy-to-Rule interface that ingests your AI policies (PDF or DOC) and suggests concrete browser guardrails you can customize.
• Built-in Regex detectors that identify risky data patterns (emails, IDs, sensitive markers) before they leave the browser.
• The Govnr Browser Extension that enforces an Observe → Warn → Narrow Block approach, delivering explainable, user-friendly controls.
• Automated generation of human-readable CSV logs mapped to compliance frameworks such as SOC 2, ISO 27001, HIPAA, GDPR, and SOX, enabling cross-functional teams to demonstrate alignment without writing code.

With Govnr, your agentic AI solutions can help users get work done efficiently while ensuring that “the AI did it” never becomes the default root cause of incidents. This structured approach supports a risk-based AI governance framework that prioritizes accuracy, data protection, and accountability—essential for the future of safe AI adoption.

---

By understanding and managing agentic LLMs and user-assumption events, organizations can harness the power of generative AI and autonomous systems responsibly. Implementing effective AI governance, continuous risk assessment, and automated monitoring will help your enterprise create valuable insights, avoid costly mistakes, and maintain trust in your AI technologies and digital products.