AI security,
one workflow at a time.
Prepare a document for AI, test a data protection control, or make a policy decision. Start with the specific job in front of your team.
Find the procedure you need
Each playbook includes a synthetic scenario, practical steps, approval checks and questions for your evaluation.
Showing 52 of 52 playbooks
Document workflows
Prepare a contract for AI review
Prepare a contract excerpt for AI review while preserving party roles, defined terms and clause references. Check hidden identifiers before sharing.
Read playbookDocument workflows
Prepare payroll workbooks for AI
Separate a payroll formula question from employee records. Check hidden sheets, linked cells and useful calculations before sending a workbook to AI.
Read playbookDocument workflows
Prepare invoices for AI processing
Build an invoice extraction example without sharing payment instructions. Preserve line-item relationships and verify text, images and remittance details.
Read playbookDocument workflows
Prepare sales pipeline data for AI
Minimize a sales pipeline export while retaining stage history and useful trends. Review contact notes, account clues and customer-level reconstruction risks.
Read playbookDocument workflows
Prepare support tickets for AI
Prepare minimized support examples for AI theme analysis. Review quoted threads, logs and attachments while keeping the issue and resolution sequence useful.
Read playbookDocument workflows
Prepare HR investigation notes for AI
Decide whether an HR investigation task can use a synthetic excerpt. Preserve attribution and uncertainty without exposing witnesses or case allegations.
Read playbookDocument workflows
Prepare resumes for AI summarization
Create a minimized resume excerpt for a factual summary. Review contact details, identifying career clues and unsupported inferences before sharing or reuse.
Read playbookDocument workflows
Prepare incident reports for AI review
Build an incident-review excerpt without live secrets or unnecessary infrastructure details. Preserve event order and check generated claims against evidence.
Read playbookDocument workflows
Prepare M&A documents for AI review
Scope an M&A review around an approved excerpt or synthetic example. Separate transaction identity, commercial sensitivity and permission to use an AI provider.
Read playbookDocument workflows
Prepare insurance claim files for AI
Prepare a bounded claim chronology or document checklist for AI. Review claimant identifiers, narratives and attachments without delegating a coverage decision.
Read playbookDocument workflows
Prepare board papers for an AI summary
Scope a board-paper summary around authorized material. Preserve decisions, caveats and financial meaning while excluding restricted appendices and personal details.
Read playbookDocument workflows
Prepare pricing models for AI analysis
Separate a pricing formula question from confidential rates and negotiation logic. Build synthetic cases, inspect workbook dependencies and verify the proposed analysis.
Read playbookDocument workflows
Prepare audit working papers for AI
Prepare a synthetic or approved audit excerpt that preserves evidence references and unresolved exceptions. Keep client identifiers and original workpapers separate.
Read playbookDocument workflows
Prepare property documents for AI
Create a property-document excerpt without unnecessary addresses or ownership clues. Preserve unit relationships and task context while reviewing maps and attachments.
Read playbookDocument workflows
Prepare participant research data for AI
Scope AI assistance for participant research using approved excerpts or synthetic responses. Review consent scope, indirect identifiers and quotation fidelity.
Read playbookDocument workflows
Prepare procurement bids for AI comparison
Build a controlled bid-comparison extract with consistent criteria and source references. Protect supplier contacts and commercial details without inventing equivalence.
Read playbookDocument workflows
Prepare meeting transcripts for AI
Prepare an approved transcript excerpt for action extraction. Review speaker identity, off-topic personal details and inaccurate transcription before generating a summary.
Read playbookDocument workflows
Prepare healthcare administration files for AI
Prepare a bounded healthcare administration extract without unnecessary case identifiers. Preserve workflow status and review indirect identification before AI use.
Read playbookControl evaluation
Test typed and pasted AI prompts separately
Evaluate typing, paste and submission as separate AI DLP paths using synthetic fixtures, repeatable controls and evidence of the final result.
Read playbookControl evaluation
Evaluate every in-scope AI file upload path
Build a repeatable AI upload test covering attachment buttons, drag and drop, multiple files and unsupported inputs without assuming equal protection.
Read playbookControl evaluation
Verify DOCX redaction without losing document structure
Evaluate a redacted Word document for protected text, tables, headers, comments and editing usability with a synthetic document review protocol.
Read playbookControl evaluation
Test spreadsheet redaction beyond visible cells
Evaluate redacted Excel workbooks for hidden content, formula behavior and retained business meaning using a controlled synthetic spreadsheet.
Read playbookControl evaluation
Evaluate text PDFs and scanned PDFs separately
Test PDF redaction using text, scanned and mixed documents, with checks for selectable content, OCR limitations and usable output.
Read playbookControl evaluation
Measure false positives against an agreed task set
Build a labeled synthetic evaluation set to distinguish AI DLP false positives, missed detections and genuine disagreements about policy.
Read playbookControl evaluation
Measure the delay employees experience when submitting to AI
Measure policy-decision and submission delays separately from AI response generation with a repeatable synthetic latency evaluation.
Read playbookControl evaluation
Distinguish a hard block from a user override
Evaluate warning, override, block and redaction decisions against an explicit AI-use policy with safe synthetic submission tests.
Read playbookControl evaluation
Verify AI policy decisions for different teams and roles
Test identity mapping, group overlap and policy changes with dedicated accounts before applying different AI-use rules across teams.
Read playbookControl evaluation
Recheck AI DLP after a browser update
Use a small regression suite to confirm prompt, attachment and policy behavior after browser or extension changes on managed devices.
Read playbookControl evaluation
Test AI DLP when managed devices leave the office
Evaluate approved AI controls across office and off-site network conditions without assuming endpoint or network architecture guarantees roaming coverage.
Read playbookControl evaluation
Compare browser and native AI workflows using matching tests
Build a matched browser-versus-desktop AI evaluation that separates visibility, prompt prevention and file handling for each supported path.
Read playbookControl evaluation
Verify that an AI security alert reaches its intended owner
Trace a synthetic AI policy event through a supported alert integration and confirm delivery, interpretation and ownership without assuming an event schema.
Read playbookControl evaluation
Evaluate AI DLP failure and recovery before rollout
Use approved isolated fault scenarios to assess AI submission behavior, user guidance and recovery without assuming fail-open or fail-closed operation.
Read playbookControl evaluation
Validate what an AI security event needs to contain
Review supported event payloads, access and retention against a specific investigation purpose using synthetic AI security events.
Read playbookControl evaluation
Define AI security acceptance criteria before the demo
Turn business requirements into a scoped AI security pilot with testable outcomes, evidence owners, mandatory gates and explicit unresolved cases.
Read playbookControl evaluation
Verify discovery and enforcement as separate AI capabilities
Evaluate catalog entries, observed AI use and supported prompt or file controls separately instead of treating an AI tool count as enforcement coverage.
Read playbookPolicy in practice
Review an AI policy exception before a client deadline
Decide whether an urgent client task needs an AI policy exception, a narrower dataset or an approved alternative, with an owner and expiry.
Read playbookPolicy in practice
Turn a discovered AI tool into a reviewed decision
Move a newly discovered AI application from an observation to a documented approve, restrict or defer decision based on its actual use.
Read playbookPolicy in practice
Retire an AI tool while preserving business work
Plan an AI tool retirement around business records, reusable work, integrations and access removal rather than a subscription cancellation alone.
Read playbookPolicy in practice
Set AI access boundaries for a new contractor
Clarify a contractor's approved AI accounts, devices, client-data boundaries and exit responsibilities before the first project task.
Read playbookPolicy in practice
Hand over AI policy ownership without losing decisions
Transfer AI policy ownership with a decision register, current exceptions, control responsibilities and a practical walkthrough of unresolved work.
Read playbookPolicy in practice
Prepare AI usage evidence for a board decision
Build a board briefing that separates observed AI use, tested controls, coverage gaps and decisions required without overstating what activity metrics prove.
Read playbookPolicy in practice
Triage a sensitive-data submission to AI
Establish whether a sensitive AI submission was prevented or transmitted, preserve minimal evidence and route the case to the right incident owner.
Read playbookPolicy in practice
Resolve AI account and plan mismatches
Check whether employees are using the AI account, workspace and plan that were approved before relying on reviewed settings or provider commitments.
Read playbookPolicy in practice
Roll out AI guardrails with a clear employee notice
Explain what AI guardrails do, what information the deployment handles and how employees can get help before introducing new controls.
Read playbookPolicy in practice
Choose a useful sample for an AI security pilot
Choose pilot teams by their AI workflows, data categories and endpoint paths so a successful test does not hide important deployment gaps.
Read playbookPolicy in practice
Review AI use when two organizations combine
Reconcile AI tools, approval records, accounts and data-sharing boundaries after a merger without assuming either organization's controls cover the other.
Read playbookPolicy in practice
Close AI access when an employee leaves
Coordinate AI account access, shared projects, connections and business-record handover when an employee leaves, with explicit verification by each owner.
Read playbookPolicy in practice
Resolve the risks of a shared AI account
Review shared AI logins, workspace access and conversation visibility, then establish accountable access without interrupting the team's legitimate work.
Read playbookPolicy in practice
Recheck an AI tool after a vendor change
Reassess an approved AI workflow when a provider changes its product, terms, permissions or data handling, using the original approval as the baseline.
Read playbookPolicy in practice
Separate AI training terms from data-sharing decisions
Assess what a no-training commitment does and does not answer before permitting sensitive prompts or files to reach an AI service.
Read playbookPolicy in practice
Turn MCP inventory findings into a scoped review
Interpret MCP configuration findings, verify ownership and permissions, and assign remediation without confusing static inspection with live blocking.
Read playbookPolicy in practice
Resolve an AI DLP policy dispute with evidence
Review a disputed AI block or warning by separating detection accuracy, policy intent and workflow fit, then verify any scoped change.
Read playbookTurn a checklist into a tested workflow
Discuss your AI tools, devices and data handling requirements with Aona. Define what success needs to look like before evaluating controls.