Governance, security, and trust
Execution systems only hold trust over time when the plan reflects reality, changes are visible, ownership is unambiguous, and visibility is appropriate for each role.
What it is
In Parallel is built around two design principles that produce those conditions: execution truth is traceable — every change is attributable to a decision, a meeting, or a confirmed action — and control stays with humans — AI proposes, people confirm. Neither principle works without the other. If changes happened silently, the plan would stop being trusted. If AI had no oversight, accountability would erode.
These principles are part of the Calm System Doctrine — In Parallel's governance approach for AI behavior, covering Simplicity, Control, Clarity, and Continuity. The four principles below are where that doctrine shows up in practice.
How it works
Four governance principles are embedded in the product:
Clear ownership Every Execution Scope has one accountable owner, and every Action Item has a named person responsible for it. Execution drift most commonly comes from implied ownership — "someone is on it," "we decided... I think." In Parallel keeps ownership explicit so accountability doesn't disappear.
Role-appropriate visibility Scope members (people doing the work and owning actions) and stakeholders (people who need visibility but not operational detail) have different access levels. This protects psychological safety and prevents the plan from becoming a performance artifact that the wrong audience scrutinizes.
Changes are explicit, not silent Execution plans update as work evolves, and each meaningful change is visible and reviewable. This prevents untracked "plan flicker" and makes execution evolution attributable to specific decisions and meetings rather than unexplained drift.
Decisions preserve the "why" The decisions log records what was decided, why, by whom, and what changed as a result. When execution changes materially, stakeholder questions can be answered with a link to the decision record and the resulting plan state — not a rewrite.
AI and human responsibilities
In Parallel maintains a clear boundary between what AI handles and what humans decide:
Domain | AI | Human |
Listening | Captures decisions, commitments, and changes from meetings | Confirms that captured items are accurate |
Connecting | Links new information to existing plan elements | Validates that connections are meaningful |
Detecting drift | Flags when execution diverges from stated intent | Decides whether drift is a problem or an intentional pivot |
Proposing updates | Generates structured plan change proposals | Approves, modifies, or rejects each proposal |
Surfacing risk | Identifies deadline conflicts and ownership gaps | Prioritizes which risks to act on and how |
Deciding | Never | Always |
Owning outcomes | Never | Always |
Deployment considerations
The Transcriber and meeting capture When introducing In Parallel to a team, address meeting capture explicitly during onboarding. Clarify what is captured (decisions, actions, risks — not individual performance), who sees meeting summaries, and that the post-meeting report is the canonical output rather than the raw transcript. Teams that understand this frame accept the tool readily; teams that don't may feel surveilled.
Security and compliance
In Parallel is certified to ISO/IEC 27001:2022 (information security), ISO/IEC 42001:2023 (AI management), and SOC 2 Type II (tested and attested). GDPR compliance is enforced by design: all customer data is processed and stored exclusively in AWS eu-central-1 (Frankfurt) and never leaves the EU. In Parallel signs a Data Processing Agreement (DPA) with all customers, with controller and processor responsibilities defined transparently.
Data is encrypted at rest using AES-256 via AWS KMS with tenant-specific keys and automatic rotation. All data in transit is protected with TLS 1.2 or higher. Access to production systems requires MFA for all console and API access; role-based access control (RBAC) is enforced throughout, with immutable audit trails. Sessions time out automatically after a period of inactivity, reducing the risk of unauthorized access from unattended devices.
Customer data is not shared with AI model vendors. AI inference runs on custom LLMs hosted within the EU. Third-party speech processing (AssemblyAI and ElevenLabs) uses EU-only endpoints. All subprocessors — including Twilio and AWS — sign DPAs and are continuously audited for Schrems II readiness.
Data retention policies are customer-defined. Meeting recordings can be deleted immediately after processing. GDPR right-to-erasure requests are fulfilled with logged, auditable verification.
For a full technical overview, contact the In Parallel team for the current security whitepaper.
Rollout approach Start with one scope that has clear ownership, a recurring meeting cadence, and a small set of members. This keeps governance simple, produces a working example quickly, and makes it easy to demonstrate the value before expanding.
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