Concern 917 · Working draft · editorial review in progress
AI-enabled insider threats
AI-enabled insider misuse occurs when someone with legitimate access uses AI tools to make unauthorized collection, analysis or disclosure of information easier.
Understanding the concern
An insider may already have access to sensitive records. An assistant with broad search or export permissions could help gather more information quickly or make unusual activity harder to notice among ordinary use. This is different from an outside attacker breaking in, though an outsider may compromise an insider account.
CISA's threat scenarios describe the general danger of abusing trusted access. Applying that concern to AI means examining permissions and actions, not treating every employee as suspicious. Think of giving a records clerk a very fast copying machine: the tool changes scale, while access rules still matter. Limited permissions, useful audit trails and proportionate review can help detect misuse without blanket surveillance.
An example
An authorized user asks a connected assistant to gather confidential files unrelated to their job and send them outside the organization.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
Evidence and its limits
The CISA scenario is about insider access generally. The AI example is a plausible mechanism, not a measured incident rate or an allegation against a specific employee.
CISA ICT Supply Chain Risk Management Task Force · Threat scenarios
Information Technology Supply Chain Threat Scenarios, version 2 ↗ (opens in a new tab)Describes malicious insiders and access misuse. AI-assisted misuse is an application of the general scenario, not a measured rate of AI insider incidents.
NIST · SP 800-161 Rev. 1 · updated 2024 · Official guidance
Cybersecurity Supply Chain Risk Management Practices for Systems and Organizations ↗ (opens in a new tab)Addresses technology supply-chain visibility, integrity and supplier risks across products and services.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- NIST AI RMF (opens in a new tab)
framework
- ISO/IEC 42001 (opens in a new tab)
standard
A question to discuss
Can the assistant access or export more information than this person needs, and would misuse be visible?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
Search for more writing
For more specific or recent material, open a search below or ask for a web summary. AI-generated summaries can contain mistakes; open the original articles to check them.