Agent-versus-Human Attribution Logging

Access through human identity occurs when a synthetic subject is configured to use a human employee’s account, session, token, credentials, browser profile, or delegated permissions to access systems or perform actions. This may occur where an employee connects an artificial intelligence tool to their mailbox, browser, source repository, cloud console, productivity suite, or business application using their own identity.

This configuration creates an elevated exposure condition because the synthetic subject inherits the employee’s access, trust, and attribution trail. Actions taken by the synthetic subject may appear in logs as ordinary human activity, even where the employee did not review each action or understand how the synthetic subject used their access.

The primary risk is identity inheritance. The synthetic subject may retrieve data, send messages, modify records, run commands, or trigger workflows using permissions granted to the human account. This makes it difficult to distinguish direct employee behavior from AI-mediated action.

Investigators should review authentication sessions, OAuth grants, connected applications, browser extensions, delegated permissions, endpoint activity, application logs, prompt and response records, tool-call logs, and user activity timelines. Particular attention should be given to AI tools operating inside authenticated user sessions, personal automation scripts, browser agents, local coding assistants, and third-party AI services connected through employee-granted access.

Investigative Relevance

Access through human identity is relevant because it defines what a synthetic subject can do by borrowing the reach of a human account. This weakens identity-based investigation, access governance, and containment because disabling or restricting the synthetic subject may require examining the employee’s sessions, devices, integrations, and delegated grants.

Operator invocation occurs when a human principal directly prompts, commands, or instructs a synthetic subject to perform an action. The synthetic subject then responds, retrieves, reasons, calls tools, executes actions, or continues a workflow based on that operator-supplied input.

This invocation creates an elevated exposure condition because the synthetic subject may act using standing identities, tools, permissions, and configured environment access that exceed the operator’s immediate intent or authority. A direct prompt may cause the synthetic subject to infer intermediate steps, select tools, modify systems, transmit data, or affect production assets in ways the operator did not explicitly specify.

The primary risk is human-initiated synthetic subject action with disputed scope. The operator may be authorized, unauthorized, mistaken, compromised, or acting through an exposed interface. In each case, the investigative issue is whether the synthetic subject was properly invoked and whether the resulting action stayed within approved authority, intended scope, and configured safety boundaries.

Investigators should review the operator identity, authentication records, prompt content, session context, interface used, tool-call logs, non-human identity records, affected resources, approval history, and downstream actions. Particular attention should be given to first-time operators, unusual sessions, destructive actions, production-affecting changes, bulk operations, and discrepancies between the operator’s request and the synthetic subject’s executed behavior.

Investigative Relevance

Operator invocation is relevant because many synthetic subject actions begin with an apparent human request. Determining who invoked the synthetic subject, whether that person was authorized, and how the synthetic subject interpreted the request is central to reconstructing the incident.

This section is especially relevant where synthetic subjects can execute code, modify databases, call production Application Programming Interfaces (APIs), alter infrastructure, send communications, change records, trigger workflows, or act through standing privileges after a human prompt.

AI-mediated fraud and misappropriation occurs when a synthetic subject, synthetic media, or AI-enabled workflow causes funds, goods, services, credentials, account access, or other value to be transferred to an unauthorized party. The transfer may be executed by the synthetic subject directly, by a human relying on AI-generated deception, or by an automated workflow influenced by AI-mediated instructions.

This adverse outcome creates organizational harm because AI can manufacture or amplify apparent authority. A synthetic subject may initiate a payment, approve a transaction, route a refund, issue a credit, or instruct a workflow to transfer value. Separately, synthetic media may impersonate executives, managers, vendors, customers, or colleagues to induce a human employee to authorize the transfer.

The primary harm is misappropriation of organizational value. The loss may involve wire transfers, vendor payments, refunds, credits, inventory release, account changes, gift cards, procurement approvals, payroll changes, or access that can later be monetized. The transaction may appear authorized because it travels through familiar business channels or appears to come from a trusted figure.

A related harm is verification failure. AI-generated voice, video, text, or agent-mediated instruction may defeat informal controls that rely on recognizing a person, trusting a meeting, or accepting a single communication channel. Investigators may need to determine whether the transaction was authorized through a structured workflow or induced through synthetic-media impersonation compromised identity, or synthetic subject action.

Investigators should review payment records, beneficiary accounts, workflow approvals, operator identity, non-human identity activity, prompt and response logs, tool-call records, communication channels, video or voice authorization records, liveness signals, session telemetry, and approval provenance. Particular attention should be given to novel beneficiaries, unusual amounts or velocity, skipped approval steps, ad hoc payment requests, agent-initiated transfer actions, and high-value instructions authorized through a single channel.

Investigative Relevance

AI-mediated fraud and misappropriation is relevant because the harmful outcome is the unauthorized transfer of value. The synthetic subject or synthetic media may not be the final payment rail, but it can supply the apparent authority, instruction, approval, or automation path that causes the transfer.

This section is especially relevant where synthetic subjects can initiate payments, route refunds, update beneficiary records, approve procurement, issue credits, interact with finance systems, generate payment instructions, or impersonate trusted organizational figures through synthetic audio, video, or text.

Identity misattribution and impersonation harm occurs when a synthetic subject acts under, imitates, or is confused with a human, service, agent, or organizational identity in a way that deceives people, systems, or records about who is acting. The harm may arise from spoofed identity, shared service accounts, synthetic media, weak agent attribution, or machine identities that are not clearly distinguishable from human users.

This adverse outcome creates organizational harm because identity is used to assign authority, trust, responsibility, and accountability. If a synthetic subject appears to be an employee, executive, service account, approved agent, vendor, or peer system, its actions or messages may be accepted as legitimate even when they are unauthorized, misleading, or harmful.

The primary harm is loss of trustworthy attribution. A synthetic subject may send communications, approve actions, access records, call tools, or modify systems under an identity that does not accurately represent the actor. This can mislead recipients, distort audit trails, weaken contractual trust, and make it difficult to determine whether a human, agent, service, or attacker-controlled identity caused the action.

A related harm is reputational and relationship damage. Third parties may rely on a synthetic message, synthetic voice, synthetic video, or agent-originated action as if it came from a trusted person or system. The organization may then face disputes, fraud loss, customer distrust, regulatory scrutiny, or operational breakdown because identity provenance was unclear or false.

Investigators should review identity records, non-human identity activity, authentication logs, service account ownership, prompt and response logs, message provenance, cryptographic signatures, session history, concurrent use, geolocation, user-agent data, and actor attribution fields. Particular attention should be given to shared identities, unmanaged agent accounts, actions logged as human but produced by a synthetic subject, impossible or concurrent use of one identity, and communications that lack verifiable agent-origin attribution.

Investigative Relevance

Identity misattribution and impersonation harm is relevant because synthetic subjects can blur the boundary between human, service, and agent action. The adverse outcome is not only that an identity was misused, but that people, systems, or records were caused to trust an incorrect actor.

This section is especially relevant where synthetic subjects communicate externally, approve workflows, transact through service accounts, operate under delegated human authority, interact with other agents, or generate synthetic audio, video, or text that mimics a trusted organizational figure.

A developer coding assistant is an artificial intelligence system embedded in, or connected to, the software development environment. This may include an integrated development environment (IDE) extension, repository-aware coding assistant, code completion tool, terminal-capable agent, or automated refactoring assistant.

This deployment pattern creates an elevated exposure condition because the assistant operates close to the software supply chain. Its output may become source code, configuration, tests, build logic, documentation, or command execution inside a project. Vulnerable logic, unsafe dependency changes, insecure configuration, or hidden backdoor functionality may then be reviewed as ordinary developer work.

The primary risk is that the synthetic subject can introduce insecure or malicious code that is later attributed to the human developer who accepted, edited, or committed it. This risk is heightened where suggestions are accepted under time pressure, generated code is difficult to review, or automated tests confirm functionality without detecting security impact.

A related risk is context poisoning. Developer coding assistants commonly use project files, comments, dependency manifests, configuration files, issue text, documentation, or local rule files as generation context. If a low-trust contributor, compromised dependency, external issue, or malicious insider places instructions into that context, the assistant may treat them as project guidance.

Investigators should review the assistant’s directive, development environment configuration, repository context sources, local instruction files, generated diffs, accepted completions, command history, dependency changes, and commit timeline. Particular attention should be given to suspicious generated code, embedded instructions in repository context, and logs that preserve both the human developer action and the synthetic subject’s contribution.

Investigative Relevance

Developer coding assistants are relevant to SITM because they can influence software that later runs in production, security tooling, customer environments, or internal infrastructure. The synthetic subject may not deploy the code directly, but it can shape the implementation a human developer reviews and commits.

A browser or desktop agent is an autonomous AI agent that operates through a real browser, desktop environment, or graphical user interface. It may read rendered pages, screenshots, documents, forms, or application windows, then click, type, navigate, copy, paste, upload, download, or submit information on behalf of a user.

This deployment pattern creates an elevated exposure condition because the agent acts inside the user’s authenticated session. It may interact with applications using the user’s existing cookies, tokens, permissions, and access rights, making its actions appear as ordinary user activity.

The primary risk is untrusted interface content. Any page, URL fragment, screenshot, document, form field, or rendered message the agent reads may become an instruction surface. A malicious page or document may redirect the synthetic subject into disclosing session data, submitting sensitive information, copying internal content, or revealing one-time codes while operating under the user’s authority.

Investigators should review the agent’s directive, browser session context, visited URLs, rendered content, screenshots, clipboard activity, form submissions, downloads, uploads, and application audit logs. Particular attention should be given to external pages processed before sensitive actions, unusual navigation paths, one-time code exposure, and actions taken inside authenticated sessions.

Investigative Relevance

Browser and desktop agents are relevant because they allow a synthetic subject to operate through the same interface and session as a human user. This can bypass traditional separation between advice and action, since the agent can directly interact with applications rather than only recommend steps.

Shared agent identity occurs when multiple synthetic subjects, workflows, tools, or deployments authenticate through the same service account, token, credential, or application identity.

This configuration creates an elevated exposure condition because activity cannot be cleanly attributed to a specific synthetic subject. Logs may show that a shared identity accessed data, called an Application Programming Interface (API), sent a message, or updated a record, without showing which agent or workflow caused the action.

The primary risk is attribution failure. If several synthetic subjects use the same identity, investigators may be unable to determine which one acted, whether the action was authorized, or which directive, invocation, tool, or input caused the event.

Investigators should review service account usage, token assignments, connector credentials, authentication logs, agent configuration files, workflow ownership, and identity governance records. Particular attention should be given to credentials reused across agents, vendors, environments, or business functions.

Investigative Relevance

Shared agent identity is relevant because identity separation is required to reconstruct synthetic subject behavior. Where multiple agents act through one credential, containment may require disabling several workflows at once, and accountability may remain unclear.

Unattributed tool call occurs when a synthetic subject invokes a tool without logs that clearly preserve the tool name, arguments, result, invoking synthetic subject, non-human identity, human invoker, and downstream action.

This configuration creates an elevated exposure condition because investigators may see only the final system change, not the tool call that caused it. The action may appear to originate from a service account, host application, or employee rather than a specific tool invocation.

The primary risk is loss of reconstructable evidence. Without immutable tool-call logging, investigators may be unable to determine what the synthetic subject requested, what the tool returned, what data was exposed, or whether the action matched an approved directive.

Investigators should review tool-call logs, application logs, agent traces, audit trails, non-human identity records, request identifiers, and downstream system events. Particular attention should be given to missing arguments, missing results, overwritten logs, shared service identities, and actions that cannot be tied to a specific tool call.

Investigative Relevance

Unattributed tool calls are relevant because tool use is often where synthetic subject behavior becomes operational action. This sub-section is especially relevant where agents call APIs, send messages, write records, execute commands, or interact with external services.

Delegated user account access occurs when a synthetic subject is granted access to organizational systems through a human employee’s account authorization. This may include OAuth consent, connected application approval, browser authorization, account linking, or delegated access granted through a productivity, development, or business platform.

This configuration creates an elevated exposure condition because the synthetic subject can use permissions that were originally assigned to the employee. The employee may approve the connection once, while the synthetic subject continues to retrieve data, call tools, summarize content, or perform actions within the scope of that delegated grant.

The primary risk is inherited access without equivalent oversight. A synthetic subject may access mail, documents, repositories, records, or application data through the employee’s authorization, even where the employee does not review each retrieval or action.

Investigators should review OAuth grants, connected applications, user consent records, delegated permissions, application scopes, access logs, revocation history, and vendor integrations. Particular attention should be given to broad scopes, persistent grants, third-party AI services, and delegated access that bypasses central synthetic subject governance.

Investigative Relevance

Delegated user account access is relevant because it allows a synthetic subject to operate using permissions granted to a human account. This sub-section is especially relevant where employees authorize AI tools to access mailboxes, calendars, cloud drives, source repositories, customer records, or productivity suites.

Authenticated user session access occurs when a synthetic subject operates inside an employee’s active browser, desktop, or single sign-on session. This may include browser agents, desktop agents, computer-use agents, local assistants, or automation tools that interact with applications already authenticated as the employee.

This configuration creates an elevated exposure condition because the synthetic subject can act through the employee’s live session, cookies, tokens, permissions, and application state. It may navigate pages, read rendered content, submit forms, download files, copy data, or trigger actions as if the employee performed them directly.

The primary risk is session inheritance. The synthetic subject may access systems or perform actions without separately authenticating, and logs may show ordinary employee activity rather than AI-mediated behavior.

Investigators should review browser session records, endpoint telemetry, single sign-on logs, clipboard activity, form submissions, download history, visited URLs, automation traces, and application audit logs. Particular attention should be given to agent activity inside authenticated sessions, actions following external page interaction, and events lacking a distinct synthetic subject identity.

Investigative Relevance

Authenticated user session access is relevant because it allows a synthetic subject to use the employee’s active trust state. This sub-section is especially relevant where AI tools operate through browsers, remote desktops, local applications, cloud consoles, or authenticated business platforms.

Delegated mailbox and calendar access occurs when a synthetic subject is granted permission to read, summarize, draft, send, forward, schedule, or route communications through an employee’s mailbox or calendar.

This configuration creates an elevated exposure condition because the synthetic subject can act through high-trust communication channels. It may process inbound messages, retrieve attachments, summarize threads, send replies, schedule meetings, forward information, or route calendar events using the employee’s communication context.

The primary risk is communication authority through a human identity. A synthetic subject may disclose sensitive information, send unauthorized messages, create misleading commitments, forward internal content, or schedule actions that appear to come from the employee.

Investigators should review mailbox permissions, calendar permissions, delegated access grants, OAuth scopes, sent items, forwarding behavior, calendar event history, prompt logs, and message audit records. Particular attention should be given to external recipients, sensitive attachments, unusual forwarding, automated replies, and calendar actions associated with AI tools.

Investigative Relevance

Delegated mailbox and calendar access is relevant because mail and calendar systems are trusted channels for organizational action. This sub-section is especially relevant where AI tools can send messages, forward content, schedule meetings, process attachments, or act on inbound communications through a human account.

Developer account tool access occurs when a synthetic subject can use an employee’s development accounts, tools, repositories, terminals, package registries, cloud consoles, or continuous integration and continuous delivery systems.

This configuration creates an elevated exposure condition because the synthetic subject may inherit engineering access that can affect source code, build pipelines, dependencies, infrastructure, secrets, or production services. Coding assistants, terminal agents, and repository-aware tools may act through credentials and sessions intended for the developer.

The primary risk is AI-mediated engineering change under a human account. A synthetic subject may commit code, alter dependencies, run commands, call cloud APIs, publish packages, modify configuration, or trigger builds in a way that appears attributable to the developer.

Investigators should review source control logs, commit metadata, integrated development environment telemetry, terminal history, cloud audit logs, package registry activity, continuous integration logs, and tool-call records. Particular attention should be given to generated diffs, dependency changes, package publishing, command execution, and production-affecting actions under a developer identity.

Investigative Relevance

Developer account tool access is relevant because human development credentials often carry high-impact engineering authority. This sub-section is especially relevant where synthetic subjects operate in integrated development environments, terminals, source repositories, cloud consoles, build systems, or package registries.

Privileged user access occurs when a synthetic subject operates through an administrator, security, finance, Human Resources, legal, production support, or other high-impact human account.

This configuration creates an elevated exposure condition because the synthetic subject inherits sensitive access assigned to the privileged user. It may operate inside administrative consoles, security tools, finance systems, personnel records, production environments, cloud platforms, or regulated data stores.

The primary risk is privileged action without equivalent human control. A synthetic subject may retrieve sensitive records, modify controls, approve transactions, change configurations, access secrets, run commands, or expose regulated data while appearing to act as the privileged employee.

Investigators should review privileged session logs, administrative actions, security tool activity, cloud audit records, finance approvals, personnel record access, endpoint telemetry, prompt records, and tool-call logs. Particular attention should be given to AI tool activity during privileged sessions, actions outside normal administrative workflows, and changes lacking clear human review.

Investigative Relevance

Privileged user session access is relevant because it places a synthetic subject inside the highest-impact human access paths. This sub-section is especially relevant where AI tools assist administrators, security analysts, finance users, legal staff, Human Resources personnel, production engineers, or other privileged roles.

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.

Ambiguous actor attribution occurs when available logs or records show that an action happened, but do not identify the specific synthetic subject, human operator, tool, connector, workflow, or vendor-controlled component that caused it. The evidence may be intact and accurate, but still too general to support attribution.

This condition is distinct from log deletion or trace tampering. In ambiguous actor attribution, the problem is not necessarily that evidence was removed or altered. The problem is that the recorded actor is too broad, shared, or indirect to identify the real source of the action.

The primary risk is collapsed actor identity. A record update, message, tool call, or system change may be logged under a shared service account, delegated human identity, generic application identity, or workflow bot. The log may truthfully record that the shared identity acted, but fail to show which synthetic subject, operator prompt, tool call, or invocation path produced the action.

A related risk is containment uncertainty. If investigators cannot identify the responsible actor, they may have to disable broad identities, revoke multiple connectors, suspend unrelated workflows, or take larger containment actions than necessary. Conversely, they may fail to contain the true source if the apparent identity is only a shared proxy.

Investigators should review identity provider logs, service account ownership, non-human identity records, OAuth grants, connector identities, prompt and response logs, tool-call records, session context, agent identifiers, vendor audit fields, and actor attribution metadata. Particular attention should be given to shared service accounts, missing agent IDs, actions recorded only under a human identity, multiple agents using one credential, and vendor logs that collapse model, tool, and user activity into one actor.

Investigative Relevance

Ambiguous actor attribution is relevant because synthetic subjects often act through borrowed, shared, delegated, or vendor-managed identities. Even complete logs may be insufficient if they identify only the account used, not the synthetic subject or invocation that caused the action.

This condition is especially relevant where synthetic subjects use shared service accounts, delegated user sessions, marketplace connectors, Model Context Protocol (MCP) servers, human-attributed actions, vendor-hosted AI features, or orchestration platforms with multiple agents operating through the same identity.