How to Keep Secrets and Sensitive Data Out of AI Prompts, Embeddings, Traces, and Logs

TL;DR

Sensitive data protection for AI is not a prompt-writing problem. It is a data-path control problem.

The safest operating model classifies data before it enters the AI workflow, blocks credentials and restricted records by default, redacts or tokenizes approved confidential data, preserves source permissions in retrieval, disables prompt and tool-content telemetry unless explicitly approved, scans repositories and prompt assets for secrets, encrypts every persistent store, and gives each data surface a defined retention and deletion path.

When a leak occurs, treat the exposed value as compromised. Stop capture, revoke or rotate credentials, quarantine affected indexes and telemetry, determine every replicated copy, purge or rebuild the contaminated data set, and validate that the system cannot reproduce the exposure before service is restored.

Introduction

An AI application can leak sensitive data without ever suffering a traditional database breach.

A developer pastes a production token into a troubleshooting prompt. A retrieval pipeline embeds a restricted document into a shared vector index. An agent writes tool arguments into a trace. A debug logger records the complete prompt and response. A support engineer exports telemetry to a ticket. A backup preserves data that the primary system later deletes.

Each individual action may look operationally convenient. Together, they create a distributed sensitive-data problem across systems that were rarely designed under one retention, access, and incident-response model.

This is why “do not paste secrets into AI” is necessary but insufficient. User training cannot protect a workflow that automatically copies raw content into prompts, embeddings, caches, traces, logs, evaluation sets, and long-lived observability platforms.

A production design must assume that sensitive data will eventually approach the AI boundary. The architecture should decide what happens before the model, vector database, or telemetry backend receives it.

Scenario and Operational Risk

Assume an enterprise AI assistant can answer operational questions using internal documentation and call approved tools. The application uses retrieval-augmented generation, distributed tracing, centralized logs, a prompt repository, and an evaluation pipeline.

During an incident review, the security team discovers that a complete database connection string appeared in a trace attribute. The same value was also present in the original user prompt, an application log, a failed-request dead-letter queue, and a copied troubleshooting ticket.

The first discovery is not the full incident. It is only the first confirmed location.

AI data exposure expands quickly because one request can create multiple replicas:

Surface	How sensitive data arrives	Why it persists
Prompt and conversation state	User input, system instructions, memory, retrieved context	Session history, caches, provider retention, debugging
Embeddings and vector stores	Documents, tickets, source code, records, chat transcripts	Indexes, replicas, snapshots, stale chunks
Tool calls	Arguments, results, API errors, file content	Traces, audit records, retry queues
Traces and logs	Automatic instrumentation, exception capture, request bodies	Central retention, exports, dashboards, archives
Evaluation data	Failed examples, human review sets, regression cases	Versioned test corpora and experiment tracking
Build and source systems	Prompt templates, notebooks, configuration, test fixtures	Git history, pull requests, issue comments, artifacts
Backups and exports	Snapshots, support bundles, analyst downloads	Independent retention and delayed deletion

The operational risk is not limited to disclosure. Sensitive data can also be retrieved by the wrong user, retained beyond policy, copied into a lower-trust environment, exposed to a third-party processor, or preserved after the source record was deleted.

Protection Scope and Assumptions

This runbook applies to production and preproduction AI systems that use one or more of the following:

hosted or self-managed language models
retrieval-augmented generation
vector databases or embedding services
AI agents and tool calls
prompt, workflow, or evaluation repositories
centralized logs, traces, and metrics
data loss prevention or redaction services
CI/CD pipelines and secret scanning

The runbook assumes the organization already has an identity provider, a secrets manager, encryption key management, incident response, data ownership, and a basic information-classification policy. The AI platform should consume those controls rather than invent weaker parallel versions.

This is a technical operating model, not a substitute for legal, privacy, records-management, or regulatory guidance. Retention periods, breach-notification requirements, and approved processing locations must be set by the appropriate organizational owners.

The Sensitive Data Control Path

The most important architectural decision is where policy enforcement occurs. A warning in the user interface is advisory. A system prompt telling the model not to expose data is probabilistic. A gateway that blocks or transforms content before it reaches the model is enforceable.

The reader should notice that classification and policy decisions occur before prompt assembly, embedding generation, and telemetry export. The model is not the security boundary.

A mature implementation repeats inspection at more than one point. Input controls stop sensitive content before processing. Retrieval controls prevent unauthorized context from being selected. Output controls catch generated or retrieved disclosures. Telemetry controls prevent diagnostic systems from becoming the final leak destination.

Build a Classification Model That AI Systems Can Enforce

A policy that says “protect sensitive data” is not executable. The AI data path needs a small, testable classification model with a defined action for each class.

A practical baseline is:

Classification	Typical examples	Default AI action
Public	Published documentation, approved public content	Allow
Internal	Routine procedures, non-sensitive architecture notes	Allow in approved environments with normal access controls
Confidential	Customer records, internal financial data, employee information, proprietary code	Redact, tokenize, minimize, or require an approved use case
Restricted	Regulated records, high-impact personal data, legal hold material, sensitive investigations	Deny unless a specifically approved architecture exists
Credential	Passwords, private keys, access tokens, connection strings, signing material	Deny, alert, revoke or rotate if exposure is real

Do not classify only by document. Classification can change at the field, chunk, query, and output level. A public runbook can still contain a copied credential. An internal ticket can contain a restricted customer record. A tool result can combine several classes in one response.

Assign an Action, Owner, and Evidence Requirement

Each classification needs three operational properties:

Action: allow, redact, tokenize, block, quarantine, or require approval
Owner: application, data owner, platform, security, privacy, or records management
Evidence: policy decision, detector result, exception ID, deletion proof, or review record

Without ownership, every team assumes another layer is filtering the data. Without evidence, the organization cannot prove that a control ran or determine why an exception was allowed.

Put DLP and Redaction Before Prompt Assembly

Prompt assembly is where system instructions, user messages, conversation memory, retrieved documents, and tool results become one model request. It is also the last reliable point to stop data before it leaves the application boundary.

Inspect each input independently before concatenation. That preserves provenance and makes policy decisions explainable.

Separate Secrets from Instructions

Never place credentials, private endpoints, signing material, database passwords, or authorization rules inside a system prompt. A system prompt is model input, not a secrets manager or policy engine.

The safer pattern is:

store credentials in a secrets manager
provide them only to the tool runtime that needs them
keep the secret out of model-visible arguments and results
enforce authorization outside the model
return only the minimum business result to the agent

An agent that calls a billing tool should receive the approved invoice status, not the tool’s bearer token, database connection string, or raw backend response.

Choose the Transformation by Use Case

Redaction is not always the right transformation.

Block credentials and data that has no approved AI use.
Mask values when partial display is needed for a human reviewer.
Tokenize identities when the workflow needs consistent correlation without the original value.
Generalize precise attributes when a broader category is sufficient.
Encrypt data only when the workflow requires controlled reversibility and key ownership is clear.
Replace with typed labels when the model needs semantic context but not the original value.

For example, replacing a real customer identifier with [CUSTOMER_ID_1] can preserve the structure of a troubleshooting conversation without exposing the source value. The mapping must remain outside model and telemetry paths, in a tightly controlled store, when re-identification is required.

Use a Deny-by-Default Policy

The following vendor-neutral YAML shows the policy shape. It is not tied to a specific gateway. Replace the data classes, exception workflow, retention periods, and owner groups with organizational values.

policy_version: 1
policy_name: ai-sensitive-data-boundary

default_action: deny

classifications:
  public:
    prompt_action: allow
    embedding_action: allow
    telemetry_content: deny

  internal:
    prompt_action: allow
    embedding_action: allow_with_source_acl
    telemetry_content: deny

  confidential:
    prompt_action: redact_or_tokenize
    embedding_action: require_approved_dataset
    telemetry_content: deny

  restricted:
    prompt_action: deny
    embedding_action: deny
    telemetry_content: deny
    exception_required: true

  credential:
    prompt_action: deny_and_alert
    embedding_action: deny_and_alert
    telemetry_content: deny_and_alert
    incident_severity: high

telemetry:
  capture_prompt_content: false
  capture_response_content: false
  capture_tool_arguments: false
  capture_tool_results: false
  allowed_attributes:
    - service.name
    - deployment.environment.name
    - gen_ai.agent.name
    - gen_ai.operation.name
    - gen_ai.provider.name
    - gen_ai.request.model
    - gen_ai.response.model
    - gen_ai.usage.input_tokens
    - gen_ai.usage.output_tokens
    - error.type

exceptions:
  require_owner: true
  require_expiration: true
  require_ticket: true
  maximum_duration_hours: 24

Successful enforcement means a blocked request never reaches the model, embedding service, retry queue, or content-bearing telemetry field. A policy that blocks the provider request but logs the rejected raw prompt has still failed.

Use Layered Detection Instead of One Regex

Sensitive-data detection should combine several methods because no single detector covers every data type.

A practical pipeline can include:

provider and credential patterns for known token formats
organization-specific patterns for internal keys and connection strings
checksums and validators for structured identifiers
named-entity recognition for personal information
dictionaries for internal project names or classified terms
entropy and context signals for unknown high-randomness secrets
allowlists for known false positives
human review for high-impact ambiguous cases

Detection systems produce false positives and false negatives. The answer is not to disable them. Tune them with labeled examples from the organization’s real data and measure both precision and recall.

Add a Sanitization Boundary in Code

The following simplified Python example shows an application-side boundary. It blocks credentials, replaces selected confidential values with typed placeholders, and returns findings separately from sanitized content. It intentionally does not log the original text.

In production, replace the example patterns with a supported DLP or PII detector, organization-specific recognizers, and a policy service. Keep the interface stable so the application does not need to know which detection engine is underneath it.

from __future__ import annotations

import re
from dataclasses import dataclass
from enum import StrEnum
from typing import Iterable


class DataClass(StrEnum):
    CONFIDENTIAL = "confidential"
    CREDENTIAL = "credential"


@dataclass(frozen=True)
class Finding:
    name: str
    data_class: DataClass
    start: int
    end: int


class SensitiveDataBlocked(RuntimeError):
    pass


DETECTORS: tuple[tuple[str, DataClass, re.Pattern[str]], ...] = (
    (
        "private_key_material",
        DataClass.CREDENTIAL,
        re.compile(r"-----BEGIN [A-Z ]+PRIVATE KEY-----"),
    ),
    (
        "generic_bearer_value",
        DataClass.CREDENTIAL,
        re.compile(r"(?i)\bbearer\s+[a-z0-9._~+/=-]{20,}"),
    ),
    (
        "email_address",
        DataClass.CONFIDENTIAL,
        re.compile(r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b", re.I),
    ),
)


def detect(text: str) -> list[Finding]:
    findings: list[Finding] = []
    for name, data_class, pattern in DETECTORS:
        for match in pattern.finditer(text):
            findings.append(
                Finding(
                    name=name,
                    data_class=data_class,
                    start=match.start(),
                    end=match.end(),
                )
            )
    return sorted(findings, key=lambda item: item.start)


def sanitize(text: str, findings: Iterable[Finding]) -> str:
    findings_list = list(findings)

    if any(item.data_class == DataClass.CREDENTIAL for item in findings_list):
        raise SensitiveDataBlocked("Credential-like content was blocked")

    output = text
    for item in sorted(findings_list, key=lambda value: value.start, reverse=True):
        replacement = f"[{item.name.upper()}]"
        output = output[: item.start] + replacement + output[item.end :]

    return output


def prepare_model_input(raw_text: str) -> tuple[str, list[str]]:
    findings = detect(raw_text)
    sanitized = sanitize(raw_text, findings)
    finding_names = sorted({item.name for item in findings})
    return sanitized, finding_names

The application should record that a credential detector blocked the request, along with a policy ID, request ID, environment, and detector version. It should not record the matched value or the surrounding raw text.

What can go wrong:

overlapping findings can create replacement errors unless the library handles spans correctly
broad patterns can block legitimate content
encoded or split secrets can bypass simple matching
language-specific PII can be missed
model-generated output can reintroduce sensitive data after input redaction

For those reasons, use the code pattern as an architectural boundary, not as a complete enterprise DLP implementation.

Protect Embeddings and Vector Stores as Sensitive Data Systems

Embeddings are not a privacy eraser. They are derived from source data and can preserve information that should remain protected. The vector store also usually contains chunk text, document metadata, source identifiers, access labels, and retrieval history.

Treat the retrieval layer as a data platform with the same rigor as a database.

Preserve Source Authorization

Every indexed chunk should retain enough metadata to enforce the source system’s permissions at query time. At minimum, include:

source system and object identifier
tenant or business boundary
data classification
owner
allowed groups, roles, or policy reference
ingestion timestamp and pipeline version
source version or content hash
retention and deletion metadata

Do not rely on the model to ignore a retrieved chunk that the user was not authorized to see. Unauthorized content should never enter the model context.

Partition High-Risk Data

Shared indexes increase the blast radius of a permission error. Use separate collections, namespaces, projects, accounts, or clusters when tenant, regulatory, geographic, or administrative boundaries require stronger isolation.

Logical filters are useful, but they are not always an adequate substitute for physical or administrative separation. The right boundary depends on the impact of a missed filter and the ability to prove isolation.

Design Deletion Before Ingestion

A source-record deletion must propagate through:

extracted text
chunk stores
embeddings
vector indexes
replicas
caches
evaluation sets
snapshots and backups

Store a stable lineage key so the platform can find every derived object for a source record. If the only way to delete one record is to rebuild the entire index, document that behavior and make the rebuild process repeatable.

Make Observability Useful Without Capturing Content

AI observability does not require full prompt capture. Operators can diagnose most availability, latency, model-routing, token, retry, and tool-performance problems with metadata.

A production-safe telemetry contract should prefer:

service, environment, agent, workflow, and prompt version
requested and response model identifiers
input and output token counts
latency and time-to-first-response
finish reason
tool name and status
retry count
policy decision and detector category
low-cardinality error type
trace and request correlation identifiers

It should exclude by default:

system instructions
user prompts
model responses
retrieved documents
tool arguments and results
authorization headers
cookies and session tokens
database queries with parameter values
full exception messages and stack-local values
user identifiers as metric dimensions

Filter at the Application and Collector

Filtering only at the telemetry backend is too late. Raw content may already have crossed networks, entered queues, or been retained by an agent or collector.

Use at least two enforcement points:

Application instrumentation: do not create sensitive attributes.
Collector or gateway: delete, hash, or transform prohibited fields before export.

Backend controls are still useful as a final guardrail, but they should not be the first place sensitive content is removed.

Treat Debug Capture as a Controlled Exception

There are cases where short-lived content capture is necessary to diagnose a production failure. That should be an exception workflow, not a permanent feature flag.

A controlled debug session should define:

incident or change ticket
named owner and approver
exact service and environment
approved data class
start and automatic expiration time
restricted destination
reduced sampling rate
deletion owner
validation that capture stopped and data was purged

Prefer synthetic or replayed sanitized requests before enabling production content capture.

Scan Source Control, Prompts, and Build Artifacts for Secrets

Secrets do not enter AI systems only through user prompts. They also appear in:

prompt templates
notebooks
evaluation examples
test fixtures
configuration files
infrastructure-as-code variables
CI/CD logs
generated documentation
pull request descriptions and comments
issue trackers
exported traces and support bundles

Enable secret scanning across the full repository history, not only the latest branch. Add push protection or an equivalent pre-receive control so known credentials are blocked before they become durable history.

Use custom patterns for organization-specific tokens and internal connection strings. Validate findings when the scanning platform supports it, but do not delay revocation of a confirmed credential while waiting for perfect classification.

A leaked secret should be rotated or revoked. Deleting the line from the repository does not make a credential safe again.

Set Retention by Surface, Not by Application

One “AI retention period” is rarely sufficient because different data surfaces serve different purposes.

Use a retention matrix such as the following as a design starting point. Final values must align with records, privacy, security, operational, and legal requirements.

Data surface	Recommended default posture	Required deletion trigger
Metrics without content	Retain long enough for capacity and trend analysis	End of approved operational period
Traces without prompt or tool content	Short operational window	Age-based deletion or incident closure
Debug traces with approved content	Hours or a few days, not routine retention	Automatic expiration plus verified purge
Application logs	Structured metadata only, shortest useful period	Age, incident closure, or policy change
Conversation memory	User- and use-case-specific	Session expiration, user request, account closure
Vector chunks and embeddings	Match source authorization and lifecycle	Source deletion, permission change, dataset retirement
Evaluation sets	Sanitized and versioned	Test retirement, consent or source deletion obligation
Security audit evidence	Governed security retention	Policy-defined expiration or legal hold release
Backups and snapshots	Encrypted and time-bounded	Backup lifecycle expiration and deletion verification

Retention should be enforceable through lifecycle policies, not calendar reminders. Every exception should expire automatically.

Verify Deletion Across Replicas

Deletion proof should answer:

Was the primary record deleted?
Was the vector entry removed or the index rebuilt?
Were caches invalidated?
Were search replicas updated?
Were evaluation copies removed?
Will backups expire within the approved period?
Can the data still be retrieved by semantic search?
Can an observability query still find the value?

A successful API deletion response is not sufficient evidence when the workflow created multiple derived copies.

Encrypt Every Persistent Store and Separate Key Ownership

Encryption does not replace redaction or access control, but it reduces the impact of storage theft, snapshot exposure, and misplaced exports.

Protect:

prompt and conversation databases
vector databases and chunk stores
object storage used for ingestion
logs, traces, and dead-letter queues
evaluation and experiment platforms
caches that persist to disk
backups, snapshots, and exports

Use encryption in transit and at rest. Prefer customer-managed keys or equivalent controls when risk, regulation, or operational ownership requires them. Separate key administrators from data administrators where practical, and restrict export, snapshot, and restore permissions.

Service identities should receive only the permissions required for their role. The embedding worker should not automatically have broad read access to every document collection. The telemetry collector should not be able to decrypt restricted source data. The model runtime should not be able to enumerate secrets.

Run Validation Before Production and After Every Material Change

Sensitive-data controls need regression tests just like application logic. Build a synthetic corpus containing fake credentials, personal information, proprietary terms, encoded values, and known false positives.

Admission-Control Tests

Confirm that:

credential-like values are blocked
restricted records require an approved exception
confidential values are transformed as expected
allowed public and internal content still works
blocked content does not appear in error logs or retry queues

Retrieval Tests

Confirm that:

a user cannot retrieve another tenant’s chunks
permission changes take effect without waiting for a full reindex when the architecture promises that behavior
deleted records cannot be recovered through semantic search
poisoned or untrusted documents are quarantined before indexing
source metadata and lineage remain intact

Telemetry Tests

Confirm that:

prompt and response content are absent by default
tool arguments and results are absent by default
authorization headers and cookies are removed
metric dimensions do not include user data or unbounded text
collector-side filters still work if an application accidentally emits a prohibited field
debug capture expires automatically

Secret-Scanning Tests

Commit synthetic test patterns in an isolated test repository and verify that scanning and push protection detect them. Do not use active credentials for control testing.

Release Gate

Fail the release when any synthetic restricted value reaches a model request, embedding record, trace, log, evaluation set, or export without the expected policy action.

Contain a Sensitive Data Leak as a Multi-Surface Incident

When sensitive content is found, do not begin by deleting the first log entry. First contain active exposure and preserve enough non-sensitive evidence to determine scope.

The decision flow below separates credential response from non-credential data response while preserving a common containment and validation path.

Containment Actions

Use the following sequence:

Classify the exposed value. Determine whether it is a credential, personal data, proprietary information, regulated content, or a combination.
Revoke or rotate active credentials immediately. Assume copying occurred even when access logs do not show misuse.
Disable content-bearing telemetry. Turn off prompt, response, tool-argument, and tool-result capture at the application and collector.
Pause affected ingestion. Stop new documents from entering the contaminated pipeline.
Restrict access. Reduce access to affected dashboards, vector collections, buckets, queues, and exports.
Quarantine affected indexes or data sets. Prevent further retrieval while scope is determined.
Preserve safe evidence. Retain identifiers, timestamps, hashes, policy versions, and access events without copying the sensitive value into the incident record.

Scope the Replication Path

Search every system that could have received the value:

original request and conversation store
model gateway and provider request records
application logs
traces and events
message queues and dead-letter queues
caches
vector chunks, embeddings, and metadata
evaluation and feedback platforms
code repositories and CI/CD artifacts
support tickets and chat channels
exports, snapshots, and backups

Search with a cryptographic hash, stable fingerprint, source record ID, or tightly restricted exact-match procedure when possible. Avoid copying the raw value into general-purpose search tools.

Eradication and Recovery

Depending on the surface, remediation may require:

deleting a prompt or conversation record
rebuilding a vector index from a clean source set
purging caches
deleting or expiring trace partitions
replacing contaminated evaluation cases
revoking shared links and exports
rotating keys and tokens
replaying sanitized transactions
updating detectors and policy rules

Restore service only after a validation test confirms that the value cannot be retrieved, reproduced, or observed through the affected paths.

Notification and Post-Incident Work

Security, privacy, legal, records management, data owners, application owners, and third-party providers may all need to participate. The incident process should determine notification obligations based on the data, affected people or systems, processing locations, contracts, and applicable requirements.

The post-incident review should produce control changes, not only awareness reminders. Ask which automated boundary failed, why the data was replicated, why retention allowed it to persist, and which test should prevent recurrence.

Common Failure Modes

Relying on a System Prompt to Protect Secrets

A prompt instruction is not deterministic access control. Secrets and authorization logic should remain outside model-visible context.

Redacting the Prompt but Logging the Original

The blocked or transformed request often appears in debug messages, exception objects, queue payloads, and trace events. Inspect the failure path, not only the successful provider call.

Treating Embeddings as Anonymous

Derived data can still be sensitive, and vector stores commonly retain source text and metadata. Apply classification, access control, encryption, lineage, and deletion.

Capturing Everything for Observability

Full content may make one debugging session easier while creating a long-lived privacy and security problem. Start with metadata, then enable narrowly scoped content capture only through an expiring exception.

Using DLP as the Only Control

Detectors miss context, encoded content, novel secret formats, and business-sensitive information that has no obvious pattern. Combine DLP with source authorization, minimization, isolation, output filtering, retention, and incident response.

Deleting Only the Primary Record

AI workflows create derived and replicated data. A complete deletion must include chunks, embeddings, caches, logs, evaluations, snapshots, and exports according to their approved lifecycle.

Keeping Permanent Exceptions

A temporary diagnostic flag becomes a permanent data-collection path unless it has automatic expiration. Exceptions need an owner, reason, scope, and end time.

Operational Ownership

Sensitive-data protection fails when every team owns one component but nobody owns the complete path.

Control area	Primary owner	Required partners
Classification and approved use	Data owner	Privacy, security, legal, records management
Prompt and input inspection	Application team	AI platform, security engineering
DLP and redaction service	Security or platform team	Application teams, data owners
Vector authorization and lifecycle	AI platform or data platform	Source-system owners, identity team
Telemetry contract and filtering	Observability platform	Application teams, security, privacy
Secrets management and rotation	Security platform	Application and infrastructure teams
Retention and deletion	Records and data governance	Platform owners, privacy, legal
Incident containment	Security incident response	All affected system and data owners

One role should be accountable for proving the end-to-end control, even when implementation spans several platforms.

Production Readiness Checklist

Before enabling production traffic, confirm:

data classes and default AI actions are documented
credentials and restricted data are denied by default
prompt assembly has an inspection and transformation boundary
secrets are externalized from prompts and model-visible tool data
retrieval preserves source permissions and tenant boundaries
embeddings, chunks, metadata, replicas, and backups have deletion paths
prompt, response, and tool-content telemetry are disabled by default
collector-side filters remove prohibited fields
debug capture requires approval and expires automatically
source control, prompt assets, notebooks, and CI/CD outputs are secret-scanned
every persistent store is encrypted and access-reviewed
retention is defined separately for each data surface
synthetic leak tests run in CI/CD and release validation
credential rotation and multi-surface containment procedures are tested
ownership and escalation paths are current

Conclusion

Keeping secrets and sensitive data out of AI systems requires more than asking users to be careful. The control must exist in the architecture.

Classify data before it enters the workflow. Block credentials and restricted content by default. Redact, tokenize, or minimize confidential data only when the use case is approved. Preserve source authorization through retrieval. Treat embeddings and vector stores as sensitive data systems. Capture observability metadata without collecting raw content. Scan code and prompt assets before they become durable history. Encrypt persistent stores, define retention by surface, and design deletion before ingestion.

When exposure occurs, assume replication. Rotate active credentials, stop capture, quarantine affected data sets, trace every derived copy, purge or rebuild contaminated stores, and prove that the value cannot be recovered before restoring normal operation.

The practical security boundary is not the model. It is the set of deterministic controls around the complete AI data path.

External References

National Institute of Standards and Technology: Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile
Canonical URL: https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence
National Institute of Standards and Technology: Guide to Computer Security Log Management
Canonical URL: https://csrc.nist.gov/pubs/sp/800/92/final
OWASP Generative AI Security Project: LLM02:2025 Sensitive Information Disclosure
Canonical URL: https://genai.owasp.org/llmrisk/llm022025-sensitive-information-disclosure/
OWASP Generative AI Security Project: LLM07:2025 System Prompt Leakage
Canonical URL: https://genai.owasp.org/llmrisk/llm072025-system-prompt-leakage/
OWASP Generative AI Security Project: LLM08:2025 Vector and Embedding Weaknesses
Canonical URL: https://genai.owasp.org/llmrisk/llm082025-vector-and-embedding-weaknesses/
OpenTelemetry: Inside the LLM Call: GenAI Observability with OpenTelemetry
Canonical URL: https://opentelemetry.io/blog/2026/genai-observability/
GitHub Docs: Secret scanning
Canonical URL: https://docs.github.com/en/code-security/concepts/secret-security/secret-scanning
Google Cloud Documentation: De-identifying sensitive data
Canonical URL: https://docs.cloud.google.com/sensitive-data-protection/docs/deidentify-sensitive-data
Presidio: Presidio Analyzer
Canonical URL: https://presidio.dataprivacystack.org/analyzer/
Presidio: Presidio Anonymizer
Canonical URL: https://presidio.dataprivacystack.org/anonymizer/

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TL;DR

Introduction

Scenario and Operational Risk

Protection Scope and Assumptions

The Sensitive Data Control Path

Build a Classification Model That AI Systems Can Enforce

Assign an Action, Owner, and Evidence Requirement

Put DLP and Redaction Before Prompt Assembly

Separate Secrets from Instructions

Choose the Transformation by Use Case

Use a Deny-by-Default Policy

Use Layered Detection Instead of One Regex

Add a Sanitization Boundary in Code

Protect Embeddings and Vector Stores as Sensitive Data Systems

Preserve Source Authorization

Partition High-Risk Data

Design Deletion Before Ingestion

Make Observability Useful Without Capturing Content

Filter at the Application and Collector

Treat Debug Capture as a Controlled Exception

Scan Source Control, Prompts, and Build Artifacts for Secrets

Set Retention by Surface, Not by Application

Verify Deletion Across Replicas

Encrypt Every Persistent Store and Separate Key Ownership

Run Validation Before Production and After Every Material Change

Admission-Control Tests

Retrieval Tests

Telemetry Tests

Secret-Scanning Tests

Release Gate

Contain a Sensitive Data Leak as a Multi-Surface Incident

Containment Actions

Scope the Replication Path

Eradication and Recovery

Notification and Post-Incident Work

Common Failure Modes

Relying on a System Prompt to Protect Secrets

Redacting the Prompt but Logging the Original

Treating Embeddings as Anonymous

Capturing Everything for Observability

Using DLP as the Only Control

Deleting Only the Primary Record

Keeping Permanent Exceptions

Operational Ownership

Production Readiness Checklist

Conclusion

External References

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