Data Loss Prevention Solution

Internal AI assistants are employee-facing artificial intelligence systems embedded into workplace environments to retrieve information, answer questions, summarize content, generate work product, or perform actions across internal systems. They may connect to knowledge bases, mailboxes, document stores, chat platforms, ticketing systems, calendars, customer relationship management systems, and source code repositories.

This deployment pattern creates an elevated exposure condition because the synthetic subject operates inside the organization’s trust boundary with standing access to internal data and tools. Service identities, delegated permissions, or broad connectors may allow it to retrieve information across many users, teams, channels, or repositories, giving it broader practical reach than any single human subject should normally possess.

The main risk is the combination of trusted internal access and untrusted or weakly trusted content. A malicious instruction may be planted in an email, shared document, calendar invite, wiki page, support ticket, chat message, or code comment, then influence the assistant when that content is later retrieved during a legitimate employee request. This is commonly described as indirect prompt injection.

Investigative Relevance

Internal AI assistants are high-trust synthetic insider patterns because they operate inside normal employee workflows. They may be treated as productivity tools, but their access, retrieval behavior, and ability to act across systems can make them significant investigative subjects.

Enterprise retrieval access is the configuration of corpora, indexes, connectors, and Retrieval-Augmented Generation (RAG) pipelines that a synthetic subject can retrieve from. A corpus is a collection of documents, records, messages, files, or other source material made available for search or retrieval. RAG is a design pattern where relevant source material is retrieved before the synthetic subject generates an answer or takes action.

This configuration creates an elevated exposure condition because retrieval defines what internal information can enter the synthetic subject’s context. If retrieval spans mailboxes, documents, tickets, customer records, wikis, source code, or other enterprise stores, the synthetic subject may combine information across repositories, sensitivity levels, and access boundaries.

The primary risk is retrieval beyond entitlement. A synthetic subject may retrieve, summarize, or expose information that the human requester is not authorized to view. This may occur through shared indexes, broad connectors, cached embeddings, service identities, incomplete query-time access checks, or retrieval pipelines that do not enforce per-user permissions.

A related risk is untrusted content in retrieval context. Externally sourced documents, inbound emails, customer records, support tickets, uploaded files, or partner content may be indexed and later retrieved into the synthetic subject’s context. If that content contains malicious or misleading instructions, it may influence the synthetic subject during an otherwise legitimate request.

Investigators should review retrieval configuration, connected corpora, index membership, connector permissions, query logs, documents returned, requester identity, access-control decisions, prompt and response records, and output destinations. Particular attention should be given to cross-boundary retrieval, sensitive content in outputs, externally sourced documents, and cases where retrieved material exceeded the requester’s entitlement.

Investigative Relevance

Enterprise retrieval access is relevant because retrieval configuration determines what information a synthetic subject can see and use. Weak retrieval boundaries may turn an assistant or agent into a bridge between restricted information and an unauthorized requester.

This section is especially relevant where synthetic subjects retrieve from enterprise search indexes, RAG pipelines, mailboxes, file stores, collaboration platforms, ticketing systems, customer relationship management platforms, source repositories, or shared document corpora.

External communication access is the configuration that allows a synthetic subject to communicate beyond the organization’s controlled environment. This may include Hypertext Transfer Protocol (HTTP) requests, webhooks, email, external Application Programming Interface (API) calls, auto-rendered links, auto-rendered images, file uploads, browser navigation, or allow-listed domains.

This configuration creates an elevated exposure condition because outbound communication can complete the path from internal data access to external disclosure. A synthetic subject that can access private data, process untrusted content, and communicate externally may be exposed to what Simon Willison describes as the “lethal trifecta” for AI agents.

The primary risk is synthetic subject egress. A manipulated prompt, retrieved document, tool response, or generated output may cause the synthetic subject to transmit sensitive data outward through a permitted channel. This may occur through obvious actions such as email or API calls, or through less visible channels such as rendered image requests, link beacons, proxy requests, markdown output, or repeated small outbound requests.

A related risk is unsafe allow-listing. Domains, Content Security Policy (CSP) rules, proxy services, or trusted destinations may become exfiltration paths if they are too broad, stale, expired, re-registered, or not tied to a specific business purpose. A destination may appear trusted to the platform while still being controllable by an attacker.

Investigators should review outbound tool calls, HTTP requests, email activity, web proxy logs, Data Loss Prevention (DLP) events, rendered links and images, destination allow-lists, CSP entries, payload sizes, request frequency, and the retrieval or prompt events preceding egress. Particular attention should be given to newly observed domains, recently expired or re-registered domains, auto-rendered resources, repeated small requests, and outbound activity following access to sensitive data.

Investigative Relevance

External communication access is relevant because outbound connectivity determines whether a synthetic subject can move information outside the environment. Even a read-only assistant may become high-risk if its output can trigger external requests or transmit sensitive content.

Data exfiltration occurs when a synthetic subject causes confidential, sensitive, regulated, proprietary, or otherwise protected information to leave its authorized boundary. The transfer may be direct or indirect, intentional or unintended, visible or hidden inside another output channel.

This adverse outcome creates organizational harm because the synthetic subject may expose data it was permitted to access but not permitted to disclose. The disclosure may involve customer records, employee data, source code, credentials, financial information, legal material, internal communications, business strategy, regulated records, or other protected information.

The primary harm is loss of control over protected data. The synthetic subject may disclose information through rendered content, connected tools, outbound communications, generated files, memory, retrieved context, cross-tenant workflows, or external services. The path may not resemble a conventional file export or human-initiated transfer.

A related harm is attribution and scoping difficulty. Data exfiltration by a synthetic subject may be distributed across prompts, retrieval events, tool calls, rendered output, browser fetches, application logs, and downstream systems. Investigators may need to reconstruct both what data was exposed and which synthetic subject action caused it to leave the authorized boundary.

Investigators should review prompt and response logs, retrieval records, tool-call logs, generated artifacts, outbound communications, web proxy records, Data Loss Prevention (DLP) events, browser or application rendering logs, memory reads, connector logs, and destination records. Particular attention should be given to sensitive data appearing in external requests, messages, files, tool arguments, generated output, memory disclosures, or cross-boundary records.

Investigative Relevance

Data exfiltration is relevant because it is one of the most direct adverse outcomes a synthetic subject can produce. The same synthetic subject may have legitimate access to protected information for one purpose while causing unauthorized disclosure through another channel.

This section is especially relevant where synthetic subjects can retrieve enterprise data, summarize documents, access mailboxes, call tools, send communications, generate files, render links or images, interact with external services, or operate across users, tenants, vendors, or organizations.

An internal knowledge assistant is an employee-facing artificial intelligence system that answers questions using internal documents, wikis, file stores, tickets, policies, procedures, and other indexed sources. It is commonly implemented using Retrieval-Augmented Generation (RAG), where relevant source material is retrieved before an answer is generated.

This deployment pattern creates an elevated exposure condition because the retrieval index becomes part of the synthetic subject’s instruction surface. Any indexed document, ticket, page, comment, or shared file may later enter the assistant’s context. If that content contains malicious or misleading instructions, the assistant may treat them as relevant during an ordinary employee query.

The primary risk is that a planted or low-trust document can influence answers drawn from higher-trust material. A malicious outsider or low-trust insider may not need direct access to sensitive repositories if they can place content somewhere the assistant indexes.

A related risk is permission and access-boundary failure. The assistant may retrieve, summarize, or infer information from documents the human requestor is not entitled to view. This can occur through a shared service identity, broad retrieval index, cached embeddings, inherited connector permissions, or weak query-time access controls.

Investigators should review the assistant’s directive, retrieval configuration, indexed sources, ingestion process, access controls, source ranking, logs, and retrieved source records. Particular attention should be given to retrieval outside the employee’s entitlement, embedded instructions in indexed material, and answers that are not grounded in approved sources.

Investigative Relevance

Internal knowledge assistants are relevant to the SITM because they convert internal document stores into conversational answers at workforce scale. Their retrieval process may combine content across repositories, trust levels, and access boundaries in ways that employees cannot easily observe.

A mailbox agent with send authority is an internal artificial intelligence assistant that can read, draft, reply, forward, schedule, route, or send messages on behalf of an employee. Unlike a read-only mailbox assistant, it can act through the employee’s communication identity.

This creates an elevated exposure condition because the assistant combines mailbox access with outbound action. A malicious instruction hidden in an email, attachment, calendar invite, or message thread may cause the synthetic subject to disclose information, forward sensitive content, send unauthorized replies, or route messages externally.

The primary risk is that write and send capability collapses the gap between data exposure and action. A single indirect prompt injection may retrieve sensitive information and transmit it outward. Because messages may be sent under the employee’s identity, the activity can appear authorized, making attribution and containment harder.

A related risk is attributed communication. If the assistant sends or drafts from a named employee’s mailbox, a manipulated response may appear to be that individual’s deliberate statement. This can expose the organization to disputes, claims, regulatory scrutiny, or reputational harm where the response includes an inaccurate commitment, disclosure, approval, or inappropriate language.

Investigators should review the assistant’s directive, mailbox permissions, send authority, forwarding rules, delegated access, logs, retrieved message context, outbound messages, attachments, recipients, and calendar actions. Particular attention should be given to external messages, unusual forwarding, sensitive content in replies, and outbound actions caused by retrieved or embedded instructions.

Investigative Relevance

Mailbox agents with send authority are relevant because they allow a synthetic subject to act through a trusted communication channel. The assistant may expose information, communicate, approve, schedule, or route activity under the apparent authority of an employee.

Tool-mediated data exfiltration occurs when a synthetic subject causes protected information to leave the organization through a connected tool, connector, plugin, Application Programming Interface (API), webhook, Model Context Protocol (MCP) server, or external service.

This adverse outcome creates organizational harm because the data leaves through an action path the synthetic subject was permitted to use. The transfer may appear as an ordinary tool call, integration event, workflow update, or API request rather than a direct data export.

The primary harm is unauthorized disclosure through tool authority. A synthetic subject may pass sensitive data as a tool argument, include it in an API payload, send it to a webhook, write it into a third-party system, or route it through a connector that transmits data outside the approved boundary.

A related harm is attribution difficulty. Logs may show that a tool or service account performed the transfer, while the underlying cause was a synthetic subject decision, prompt, retrieved content, or tool-output chain. Investigators may need to reconstruct the sequence from prompt to tool call to external destination.

Investigators should review tool-call logs, connector records, API payloads, webhook destinations, MCP server activity, non-human identity records, outbound network logs, Data Loss Prevention (DLP) events, source-content provenance, and destination ownership. Particular attention should be given to sensitive data in tool arguments, unexpected external destinations, newly added tools, unusual webhook calls, and tool use following retrieval of protected information.

Investigative Relevance

Tool-mediated data exfiltration is relevant because connected tools allow a synthetic subject to move data beyond its immediate response surface. The harmful outcome is the transfer of protected information through a configured action channel.

This sub-section is especially relevant where synthetic subjects can call APIs, send webhooks, use MCP servers, write to SaaS platforms, upload files, send messages, update tickets, create records, or interact with external tools that accept model-provided content.

Communication-channel data exfiltration occurs when a synthetic subject causes protected information to leave the organization through a messaging or collaboration channel. This may include email, chat, customer support replies, tickets, calendar invites, comments, collaboration posts, direct messages, or other communication surfaces.

This adverse outcome creates organizational harm because the data is disclosed through a channel that appears routine, trusted, or human-authored. The synthetic subject may draft, send, forward, summarize, or reply using an employee account, service identity, mailbox agent, helpdesk integration, or collaboration assistant.

The primary harm is unauthorized disclosure through ordinary communication. A synthetic subject may include sensitive data in an outbound reply, forward internal content to an external recipient, summarize protected records into a customer message, or route confidential information into a shared thread or ticket visible to unauthorized parties.

A related harm is attribution confusion. The disclosure may appear to have been made by a named employee, support representative, team mailbox, or business application. Investigators may need to determine whether the communication was directly authored by a human, drafted by a synthetic subject and approved, or sent autonomously by the synthetic subject.

Investigators should review sent messages, drafts, forwarded content, recipients, attachments, mailbox audit logs, collaboration logs, ticket history, calendar records, prompt and response logs, tool-call records, and Data Loss Prevention (DLP) events. Particular attention should be given to external recipients, unusual forwarding, sensitive content in replies, protected data copied into tickets or chats, and messages sent under a human identity without clear human review.

Investigative Relevance

Communication-channel data exfiltration is relevant because synthetic subjects increasingly operate inside trusted business communication channels. A damaging disclosure may not appear as a suspicious export; it may appear as a normal reply, ticket update, forwarded message, meeting invite, or collaboration post.

This sub-section is especially relevant where synthetic subjects can read or send email, respond to customers, update support tickets, post in chat, summarize conversations, schedule calendar events, or operate through employee communication identities.

File and artifact data exfiltration occurs when a synthetic subject causes protected information to leave the organization through a generated, modified, exported, or attached file. This may include reports, spreadsheets, code bundles, notebooks, logs, archives, screenshots, transcripts, model outputs, configuration files, or other downloadable artifacts.

This adverse outcome creates organizational harm because sensitive data may be embedded inside an artifact whose apparent purpose is legitimate. A synthetic subject may generate a report, prepare an export, attach a file, create a code archive, summarize records into a spreadsheet, or write logs containing protected information that are later downloaded, shared, or transmitted.

The primary harm is unauthorized disclosure through artifact creation. Protected data may be copied from internal sources into a new file, mixed with lower-sensitivity material, or transformed into a format that bypasses the original system’s access controls. Once created, the artifact may be easier to forward, upload, store externally, or access by unauthorized parties.

A related harm is loss of source-boundary control. The original data may have been governed by role-based access, retention, classification, or audit controls, while the generated artifact may not inherit those protections. Investigators may need to determine whether the synthetic subject preserved classification labels, access restrictions, provenance, and retention requirements when creating the artifact.

Investigators should review generated files, exports, attachments, notebooks, archives, screenshots, transcripts, temporary files, download logs, file-sharing events, Data Loss Prevention (DLP) alerts, prompt and response logs, retrieval records, and tool-call records. Particular attention should be given to sensitive data copied into new artifacts, artifacts shared externally, files created under human identities, and exports whose classification or access controls differ from the source material.

Investigative Relevance

File and artifact data exfiltration is relevant because synthetic subjects frequently generate work product from internal data. The harmful outcome may not be the original retrieval, but the creation or sharing of a new artifact that carries protected information outside its authorized boundary.

This sub-section is especially relevant where synthetic subjects can create reports, spreadsheets, summaries, logs, archives, notebooks, code bundles, screenshots, transcripts, attachments, or export files from enterprise data.

Memory and context data exfiltration occurs when a synthetic subject discloses protected information from conversation history, prompt context, retrieved documents, tool outputs, persistent memory, shared memory, task state, or other model-visible context outside its authorized boundary.

This adverse outcome creates organizational harm because context may contain sensitive material that was never intended to become an output. This may include internal messages, customer records, credentials, personal data, source material, prior prompts, tool results, retrieved records, or memory entries created during earlier sessions.

The primary harm is unauthorized disclosure of contextual information. A synthetic subject may reveal prior conversation content, summarize hidden context, expose retrieved records, disclose tool output, leak memory entries, or transmit session history to a user, tool, connector, vendor, or external destination that is not authorized to receive it.

A related harm is boundary collapse across time, users, and workflows. Context may be inherited from a prior session, another user, a shared memory store, a tool call, or an agent handoff. If the synthetic subject later exposes that context, the disclosure may not be obvious from the current prompt alone.

Investigators should review conversation history, prompt context, retrieved source records, tool outputs, memory reads, memory writes, shared context stores, session state, agent handoff records, prompt and response logs, tool-call logs, and destination records. Particular attention should be given to prior-session content appearing in current outputs, memory entries disclosed to the wrong user, tool outputs copied into responses, and context transmitted to external tools or connectors.

Investigative Relevance

Memory and context data exfiltration is relevant because synthetic subjects often operate over hidden or semi-hidden context that users and investigators may not fully see. The harmful outcome is the disclosure of that context outside its intended boundary.

This sub-section is especially relevant where synthetic subjects use persistent memory, shared memory, retrieval-augmented generation, tool outputs, long context windows, conversation summaries, agent handoffs, or cross-session state.

Cross-boundary data disclosure occurs when a synthetic subject causes protected information to move outside its authorized user, tenant, customer, vendor, organizational, or trust boundary. The data may remain inside a platform or service, but still be exposed to a party, account, agent, tenant, workflow, or organization that is not authorized to receive it.

This adverse outcome creates organizational harm because boundary controls define who may access protected information. A synthetic subject may summarize, retrieve, infer, route, or disclose data across boundaries that would normally be separated by identity, tenant, role, customer, vendor, department, legal entity, or contractual control.

The primary harm is unauthorized cross-boundary exposure. Protected information belonging to one user, customer, tenant, team, or organization may appear in another user’s output, another customer’s record, another tenant’s workflow, a vendor-accessible context, or a shared agent environment.

A related harm is loss of data sovereignty and accountability. The disclosure may occur through shared indexes, cross-tenant SaaS workflows, vendor-hosted AI features, shared memory, collaboration records, support tickets, or agent-to-agent handoffs. Investigators may need to determine which boundary was crossed, what data moved, who received it, and whether the disclosure violated access policy, contractual terms, privacy obligations, or regulatory requirements.

Investigators should review tenant logs, user entitlements, retrieval records, shared indexes, connector access, vendor audit logs, support records, memory stores, agent handoff records, prompt and response logs, and downstream recipients. Particular attention should be given to data from one customer, tenant, user, or organization appearing in another boundary’s output, record, workflow, or agent context.

Investigative Relevance

Cross-boundary data disclosure is relevant because synthetic subjects may combine data across users, tenants, systems, and organizations in ways traditional access controls were not designed to observe. The harmful outcome is not only that data was disclosed externally, but that it crossed an authorization boundary.

This sub-section is especially relevant where synthetic subjects operate in shared-tenant platforms, vendor-hosted AI services, customer relationship management systems, support tools, shared memory stores, enterprise retrieval indexes, collaboration platforms, or agent-to-agent workflows.