Authored by the LynixAI privacy lab • Updated: 2025-11-18
This blueprint documents how LynixAI builds and operates privacy-first meeting assistants. Use it as a reference when evaluating Meet.AI or benchmarking other AI copilots. Every control described here has been stress-tested with customers in regulated industries.
1. Guiding principles
Local-first capture: Meeting audio is captured from the participant’s device and processed locally whenever feasible. The assistant never joins as a bot or dial-in participant.
Data minimization: We capture only the data required to answer user prompts and delete raw audio immediately after derivations are produced.
Human agency: The participant using the assistant controls activation, redaction dictionaries, and retention schedules. No silent background recording is allowed.
2. Capture architecture
Meet.AI offers two capture approaches depending on customer preference:
Browser extension capture
A Chromium-based extension records audio output locally when the user clicks “Start private notes.” The extension encrypts snippets with AES-256 before passing them to the Meet.AI desktop bridge for temporary storage.
Desktop bridge capture
Windows and macOS agents use virtual audio drivers to capture microphone and speaker streams. They enforce a hard cap on buffer size (30 seconds) and purge the buffer after each inference request.
Tip: Security teams can request the open-source audit logs that show how buffer deletion is enforced. Logs contain event IDs, timestamps, and hashed device identifiers.
3. Encryption and key management
All snippets leaving the device are encrypted with per-session keys generated via the Web Crypto API. Keys expire after 30 minutes of inactivity.
Transport to LynixAI processing clusters uses TLS 1.3 with Perfect Forward Secrecy.
Processed transcripts and summaries are stored in customer-specific buckets encrypted at rest using AES-256-GCM. Customers can supply their own keys through AWS KMS or Azure Key Vault.
4. Redaction and privacy filters
Before any snippet is sent for AI processing, it flows through the redaction engine:
Apply default entity recognition to mask emails, phone numbers, government IDs, and payment data.
Layer customer-configured lexicons for industry-specific sensitive terms (for example, deal codes, medical record numbers, or defence project names).
Perform context scoring. If the system detects a conversation segment that matches a restricted topic, the assistant flags the snippet as “do not process” and informs the user.
Redacted segments are replaced with placeholder tokens (for example, [SENSITIVE_TERM]), ensuring downstream models never see the original values.
In-article ad placement between redaction guidance and encryption controls.
5. Regulatory alignment
This blueprint satisfies common regulatory expectations:
GDPR / UK GDPR: Lawful basis is legitimate interest for internal productivity; customers may choose to obtain consent. Data processing agreements include Standard Contractual Clauses.
HIPAA: When a Business Associate Agreement is executed, Meet.AI enforces PHI redaction, access logging, and 30-day retention by default.
CCPA/CPRA: Consumers may request access or deletion; LynixAI supplies data export tooling for administrators.
PCI DSS considerations: Redaction ensures cardholder data is never processed. Customers handling payments can schedule automatic purging of any flagged segments within 15 minutes.
6. Deployment workflow
Discovery workshop: Map meeting types, participants, and applicable regulations. Document which call flows will use AI assistance.
Policy configuration: Build redaction dictionaries, decide retention windows, and define escalation policies for flagged content.
Pilot launch: Run a controlled pilot with champions from each business unit. Capture baseline metrics (time spent on notes, follow-up speed, compliance checks).
Review and iterate: Analyse assistant responses, adjust prompts, and confirm that audit logs match expectations.
Production rollout: Expand access, provide training, and integrate retention data with your governance tooling.
7. Audit checklist
Use the following yes/no checklist before approving deployment:
Have capture methods been documented and approved by security and legal stakeholders?
Is there a written retention policy with named owners and review cadence?
Does the redaction dictionary include industry-specific identifiers?
Are incident response steps defined for misrouting or policy violations?
Have employees completed training that explains activation boundaries and acceptable use?
Is there a process to review model output for hallucinations or bias?
8. Frequently asked questions
Does Meet.AI store entire meeting recordings?
No. By default, we retain only the snippets required to generate responses. Customers may elect to store full transcripts for a limited time, but this setting is off unless explicitly enabled.
Can we host the processing pipeline ourselves?
Enterprise plans support customer-managed infrastructure. We provide Terraform modules and documentation for AWS and Azure deployments so data never leaves your environment.
How do you handle subject access requests?
Administrators can export an individual’s prompts, responses, and audit entries. We respond to regulator enquiries within legally mandated timelines and coordinate deletion when requested.
9. Putting the blueprint into action
Implementing AI responsibly requires more than technical safeguards. Align stakeholders on desired outcomes, assign owners for ongoing governance, and iterate quickly with real-world usage data. LynixAI provides workshops, configuration reviews, and quarterly health checks so your teams can adapt as regulations evolve.