Non-Human Identity Behavioral Baseline Monitoring

Access through Non-Human Identity (NHI) occurs when a synthetic subject authenticates or acts using a machine, service, workload, application, or agent identity rather than a direct human login. This may include service accounts, application credentials, OAuth tokens, Application Programming Interface (API) keys, cloud roles, connector credentials, or Model Context Protocol (MCP) server secrets.

This configuration creates an elevated exposure condition because the synthetic subject’s practical capability is defined by the identity and credentials assigned to it. If those credentials are shared, long-lived, over-scoped, or poorly inventoried, the synthetic subject may gain standing access that is difficult to attribute, revoke, or monitor.

The primary risk is unmanaged authority. A synthetic subject may retrieve data, call tools, send messages, update records, or perform workflow actions through credentials that exceed its intended purpose. Where multiple agents share one service account or hard-coded secret, investigators may be unable to determine which synthetic subject acted, which workflow invoked it, or which system granted the access.

Investigators should review the synthetic subject’s assigned identities, credential storage, token lifetime, scopes, authentication logs, service account ownership, vault records, repository secrets, configuration files, and access review history. Particular attention should be given to shared service accounts, orphaned tokens, hard-coded secrets, dormant credentials, privileged scopes, and identities not represented in asset or identity governance inventories.

Investigative Relevance

Access through Non-Human Identity is relevant because identity configuration determines what a synthetic subject can access and perform. Weak NHI configuration may turn a limited assistant, embedded feature, or agent into a privileged actor with access beyond its operational purpose.

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.

Deconstructed and staged invocation occurs when a harmful objective is deliberately broken into a sequence of prompts, subtasks, role-play frames, or staged inputs that appear benign when reviewed individually. The synthetic subject may comply with each request in isolation, while the sequence composes into an unauthorized, prohibited, or harmful action.

This invocation creates an elevated exposure condition because the effective instruction is distributed across time. No single prompt may contain the full objective, but the chain may cause the synthetic subject to perform reconnaissance, generate instructions, prepare tools, retrieve sensitive data, call systems, or complete a workflow that would have been blocked if requested directly.

The primary risk is cumulative intent concealment. A user may launder a prohibited objective through small technical questions, partial requests, pretextual framing, defensive role-play, or staged task handoffs. The synthetic subject may treat each step as ordinary assistance while progressively assembling the capability, context, or action path needed for the final harmful outcome.

A related risk is machine-paced staging. The sequence may show non-human timing, repetitive phrasing, short technical prompts, or sustained task progression without normal human exploration or browsing rhythm. These patterns may indicate that the synthetic subject is being driven through a deconstructed campaign rather than a legitimate interactive task.

Investigators should review the full prompt sequence, session history, cross-session activity, task tree, tool-call logs, non-human identity records, stated reasoning, role-play framing, and downstream actions. Particular attention should be given to individually benign prompts that form a recognizable chain, repeated short technical requests, pretextual security-testing claims, escalation from reconnaissance to exploitation or exfiltration, and actions whose purpose becomes clear only when the sequence is reconstructed.

Investigative Relevance

Deconstructed and staged invocation is relevant because synthetic subject behavior may be shaped by a campaign rather than a single prompt. The investigation must evaluate the cumulative objective of the interaction, not only whether each individual invocation appeared permitted.

This section is especially relevant where synthetic subjects assist with technical workflows, security analysis, coding, data retrieval, account operations, investigation tasks, tool use, or multi-step planning that can be deconstructed into benign-looking stages.

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.

Destructive system or data action occurs when a synthetic subject deletes, overwrites, wipes, disables, or otherwise damages systems, data stores, production assets, or operational environments outside its authorized scope.

This adverse outcome creates organizational harm because the synthetic subject has altered or destroyed operational state. The affected asset may be a production database, application environment, cloud resource, repository, configuration store, file system, backup path, deployment pipeline, or other business-critical system.

The primary harm is loss of availability or integrity. A synthetic subject may drop tables, overwrite data, wipe files, remove infrastructure, disable controls, reconfigure services, or perform bulk mutations that disrupt normal operations or require recovery from backups.

A related harm is false operational assurance. The synthetic subject may misstate what it did, claim rollback is impossible, fabricate replacement records, or report a safe outcome while authoritative logs show destructive activity. Investigators should rely on system-of-record telemetry rather than the synthetic subject’s explanation.

Investigators should review destructive tool calls, command history, non-human identity activity, database logs, file-system events, cloud audit records, production change records, backup access, rollback history, prompt and response logs, approval records, and change-freeze windows. Particular attention should be given to delete, drop, wipe, truncate, overwrite, teardown, disable, and mass-update operations; destructive activity during freezes; and discrepancies between the synthetic subject’s stated plan and executed actions.

Investigative Relevance

Destructive system or data action is relevant because synthetic subjects can now act directly against operational environments. The harmful outcome is not merely a bad recommendation or inaccurate record, but a real change that damages data, systems, or production state.

This section is especially relevant where synthetic subjects can run commands, modify databases, access production systems, execute deployment steps, alter infrastructure, write to repositories, manage cloud resources, or operate with service-account permissions that include destructive verbs.

Unauthorized log or record change occurs when a synthetic subject creates, modifies, deletes, suppresses, or corrupts a log entry, business record, audit record, case note, ticket, database row, customer record, investigation record, or workflow history without proper authorization, review, or scope control.

This adverse outcome creates organizational harm because records are used to establish truth, accountability, operational status, customer history, legal position, and investigative chronology. If a synthetic subject changes a record incorrectly or without authority, the organization may rely on inaccurate information or lose the ability to reconstruct what happened.

The primary harm is record integrity loss. A synthetic subject may overwrite values, fabricate entries, delete records, modify timestamps, alter case notes, misclassify tickets, change customer information, suppress audit detail, or create false operational history. The affected record may then be treated as authoritative by employees, customers, auditors, regulators, or downstream systems.

A related harm is investigative distortion. Unauthorized changes to logs or records may obscure the original event, misattribute responsibility, conceal synthetic subject action, or create a false narrative of approval, completion, rollback, or recovery.

Investigators should review before-and-after record values, audit logs, change history, tool-call records, non-human identity activity, user session records, prompt and response logs, affected systems, approval history, and downstream workflow events. Particular attention should be given to deleted entries, changed timestamps, fabricated records, bulk updates, missing audit trails, and record changes that do not match the operator’s stated request or approved workflow.

Investigative Relevance

Unauthorized log or record change is relevant because synthetic subjects increasingly act inside systems of record. The harmful outcome may be an inaccurate customer record, altered ticket, fabricated dataset, changed case note, deleted log, or modified audit history.

This section is especially relevant where synthetic subjects can update customer relationship management platforms, ticketing systems, databases, audit logs, case management tools, investigation platforms, human resources systems, finance records, compliance systems, or workflow histories.

A privileged engineering agent is an autonomous AI agent granted write access to engineering or operational systems. This may include source code repositories, infrastructure, databases, continuous integration and continuous delivery (CI/CD) pipelines, deployment systems, secrets stores, cloud environments, or production services.

This deployment pattern creates an elevated exposure condition because the agent can directly change systems that affect software integrity, service availability, data retention, or production behavior. Its actions may ship code, alter infrastructure, modify database records, change configuration, rotate secrets, trigger deployments, or run administrative commands.

The primary risk is high-impact standing access. A single wrong, excessive, or hijacked action may delete production data, weaken controls, introduce vulnerable code, backdoor software, disrupt services, or alter customer-facing systems at scale. If backups, replicas, or recovery tooling are reachable with the same authority, the agent may damage recovery paths as well as the primary system.

Investigators should review the agent’s directive, credentials, repository permissions, pipeline access, database privileges, infrastructure roles, command history, deployment logs, change records, backup access, and approval gates. Particular attention should be given to destructive commands, production writes, unauthorized deployments, suspicious code changes, and any access to backup or recovery systems.

Investigative Relevance

Privileged engineering agents are relevant because they place a synthetic subject inside high-impact engineering and operations workflows. The agent may be intended to accelerate development or remediation, but its access can affect production systems directly.

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.

Long-lived agent credential occurs when a synthetic subject uses a token, secret, key, certificate, or service credential that remains valid for an extended period. This may include static Application Programming Interface (API) keys, persistent OAuth tokens, stored connector secrets, or credentials without automatic expiry.

This configuration creates an elevated exposure condition because leaked, copied, or misused credentials may remain usable long after the original deployment, workflow, or tool action. A long-lived credential can allow continued access even where the synthetic subject’s purpose has changed or the responsible owner is no longer monitoring it.

The primary risk is persistent unauthorized access. If a long-lived agent credential is exposed through logs, repositories, configuration files, browser storage, vendor tooling, or Model Context Protocol (MCP) server settings, an unauthorized party may act with the synthetic subject’s permissions until the credential is discovered and revoked.

Investigators should review token age, expiry settings, credential rotation history, vault records, access logs, connector configuration, repository exposure, and recent authentication activity. Particular attention should be given to credentials with no expiry, stale tokens, and credentials used after a deployment or integration was retired.

Investigative Relevance

Long-lived agent credentials are relevant because they extend the window in which a synthetic subject’s access can be misused. This sub-section is especially relevant where credentials grant access to customer data, production systems, mailboxes, cloud resources, or external tools.

Over-scoped agent access occurs when a synthetic subject is granted permissions beyond what is required for its approved function. This may include broad read access, write permissions, administrative scopes, cross-repository access, tenant-wide connectors, or access to data classes unrelated to the task.

This configuration creates an elevated exposure condition because any defective directive, manipulated invocation, compromised connector, or unsafe tool call may operate across a larger scope than necessary. The synthetic subject’s practical capability is defined by what its credentials allow, not by what its prompt says it should do.

The primary risk is excessive blast radius. A synthetic subject intended to summarize tickets, answer questions, or process records may be able to retrieve confidential documents, access customer data, modify systems, or transmit information outside its intended boundary.

Investigators should review assigned scopes, role grants, connector permissions, cloud roles, data access policies, tool permissions, and actual access patterns. Particular attention should be given to tenant-wide access, broad wildcard permissions, production write access, and access to sensitive repositories that are not required for the synthetic subject’s function.

Investigative Relevance

Over-scoped agent access is relevant because excessive permission turns routine AI behavior into high-impact operational risk. It is especially relevant where a low-risk assistant, embedded feature, or workflow agent has access normally reserved for privileged users or production services.

Orphaned agent identity occurs when a synthetic subject’s credential, service account, connector identity, token, or cloud role remains active after the agent, workflow, vendor tool, or integration it supported has been retired, replaced, or abandoned.

This configuration creates an elevated exposure condition because the identity may retain access without an active owner, business purpose, or monitoring expectation. Authentication by the orphaned identity may be mistaken for legacy system behavior or ignored because no current team clearly owns it.

The primary risk is uncontrolled residual access. An orphaned identity may still retrieve data, call Application Programming Interfaces (APIs), access cloud resources, or operate tools after the original synthetic subject is no longer in use. The access may be later repurposed, or become a candidate for misuse, causing validation of legitimate activity difficult, as well as attribution.

Investigators should review identity ownership, last-use timestamps, deployment records, connector inventories, access review results, vendor integrations, and decommissioning records. Particular attention should be given to active credentials linked to retired agents, inactive projects, disabled applications, former vendors, or undocumented workflows.

Investigative Relevance

Orphaned agent identities are relevant because they preserve access after operational need has ended. This sub-section is especially relevant during incident containment, vendor offboarding, platform migration, and synthetic subject decommissioning.

Excessive agency occurs when a synthetic subject is configured with a combination of permissions, tools, functions, or action paths that exceed its approved operational purpose. This may include broad read access, write capability, administrative functions, production access, external communication authority, or permission to call high-impact tools.

This configuration creates an elevated exposure condition because the synthetic subject is not only able to access information, but also to act on it. Excessive agency may allow the synthetic subject to retrieve data, modify records, send messages, trigger workflows, call Application Programming Interfaces (APIs), change configurations, or perform operational actions beyond what its role requires.

The primary risk is excessive blast radius. A flawed directive, manipulated invocation, compromised tool, or unsafe model output may result in actions that affect systems, data, customers, or business processes outside the synthetic subject’s intended scope. This is especially significant where the synthetic subject holds standing access to privileged functions or where high-impact actions do not require separate approval.

Investigators should review the synthetic subject’s permissions, tool access, functional capabilities, connector scopes, write privileges, administrative roles, external communication authority, and approval gates. Particular attention should be given to functions that allow data modification, external transmission, production change, financial action, security control alteration, or access to backup and recovery systems.

Investigative Relevance

Excessive agency is relevant because configuration determines not only what a synthetic subject can see, but what it can do. A limited assistant may become a high-impact synthetic subject if its permissions and functions allow it to act across systems without appropriate restriction.

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.

Local user credential access occurs when a synthetic subject can access credentials, tokens, keys, certificates, session files, or secrets stored in an employee’s workstation or development environment. This may include environment variables, command-line profiles, password manager access, cloud configuration files, package registry tokens, Secure Shell (SSH) keys, local application secrets, or local AI tools that can control the computer through the keyboard, mouse, browser, terminal, or graphical user interface.

This configuration creates an elevated exposure condition because the synthetic subject may discover and use credentials that were not intentionally granted to it. Local assistants, coding agents, terminal-capable agents, browser agents, or computer-use agents may inherit access to files, sessions, shells, profiles, clipboard contents, credential stores, or environment state containing sensitive credentials.

The primary risk is unintended credential use. A synthetic subject may read, copy, transmit, or apply local credentials to access systems beyond its intended scope, including source repositories, cloud environments, databases, internal tools, or production services.

Investigators should review endpoint file access, shell history, environment variables, credential stores, local configuration files, password manager events, source repository access, browser activity, clipboard activity, and tool runtime permissions. Particular attention should be given to AI tools with file-system access, terminal access, browser control, graphical user interface control, broad workspace access, or access to developer credential locations.

Investigative Relevance

Local user credential access is relevant because credentials stored for human convenience may become usable by a synthetic subject. This sub-section is especially relevant where local AI assistants, coding assistants, terminal agents, browser agents, or computer-use agents operate on employee workstations with access to the same local resources as the user.

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.

Unauthorized operator invocation occurs when a person causes a synthetic subject to act without legitimate authority to invoke it. This may involve stolen credentials, exposed interfaces, misconfigured access controls, abandoned accounts, weak authentication, public endpoints, or impersonation of an authorized user.

This invocation creates an elevated exposure condition because the synthetic subject may treat the request as valid and execute it using its standing identity, tools, permissions, and configured environment access. The unauthorized operator may not need direct access to the underlying systems if the synthetic subject can act on their behalf.

The primary risk is unauthorized use of synthetic subject authority. A person outside the approved operator set may use the synthetic subject to retrieve data, call tools, update records, send messages, run commands, or trigger workflows that they could not perform directly.

Investigators should review authentication records, session history, operator identity, access-control decisions, interface exposure, prompt logs, tool-call logs, affected systems, and downstream actions. Particular attention should be given to unusual operator accounts, impossible travel, first-time invocation, public interface exposure, failed authentication attempts, abandoned accounts, and tool calls inconsistent with the apparent operator’s role.

Investigative Relevance

Unauthorized operator invocation is relevant because the synthetic subject may become an access broker for an unauthorized person. The person does not need the same direct system permissions as the synthetic subject if they can successfully invoke it.

This section is especially relevant where synthetic subjects are exposed through internal web applications, chat interfaces, browser extensions, developer tools, workflow endpoints, Application Programming Interfaces (APIs), or vendor platforms with weak operator authentication or authorization.

Shared Non-Human Identity (NHI) attribution gap occurs when multiple synthetic subjects, users, sessions, tools, or workflows act through the same service account, credential, token, connector identity, or application identity. The logs may show that the shared identity acted, but not which synthetic subject, operator prompt, session, or human principal caused the action.

This condition frustrates investigation because the evidence is too broad to support reliable attribution. The issue is not necessarily missing logs or deleted records. The issue is that the recorded actor is a shared identity that collapses multiple possible actors into one audit trail.

The primary risk is loss of accountability. A database update, message, tool call, file access, Application Programming Interface (API) request, or workflow action may be logged under a single shared NHI. Investigators may be unable to determine whether the action was caused by one agent instance, another agent instance, an automated workflow, a connected tool, or a human operator acting through the same credential.

A related risk is containment uncertainty. If the responsible synthetic subject cannot be identified, responders may need to disable the entire shared identity, interrupt multiple workflows, revoke broad connectors, or suspend legitimate operations. Conversely, containment may fail if the true actor continues to operate through the same shared identity after one suspected agent is disabled.

Investigators should review service account ownership, credential assignment, token issuance, connector identity records, non-human identity activity, prompt and response logs, session identifiers, agent identifiers, human-principal binding, OAuth grants, Privileged Access Management (PAM) records, and downstream audit logs. Particular attention should be given to one credential exhibiting multiple behavioral patterns, concurrent use of the same identity, actions with no resolvable originating principal, static long-lived credentials, and service accounts used by multiple synthetic subjects.

Investigative Relevance

Shared NHI attribution gap is relevant because synthetic subjects often act through machine identities rather than direct human accounts. When multiple agents or workflows share one NHI, even complete logs may be insufficient to identify the true source of an action.

This condition is especially relevant where synthetic subjects use shared service accounts, static API keys, marketplace connectors, Model Context Protocol (MCP) servers, automation tokens, application identities, or vendor-managed credentials that do not preserve the originating user, session, agent, and invocation context.