Law Enforcement Pilot

SYNNQ
PULSE
DEMO

A concrete, end-to-end example showing metadata processing, federated learning, and compliant cross-border law enforcement coordination.

Pilot Plan & Architecture Overview

SYNNQ Pulse serves as federated AI infrastructure (server + client orchestration), not an end-user analytics suite. Any analytics mentioned refers to partner/agency-built modules that sit on top of Pulse.

A) Pilot SOW & Timeline

Objective: Show that SYNNQ Pulse can serve as the sovereign federated backbone for cross-border link-analysis/triage by powering models across local data silos—so raw personal data never leaves agency premises—while meeting EU AI Act (high-risk obligations), Law Enforcement Directive (LED) 2016/680, and adjacent rules.

Scope: 2–3 agencies in 1–2 neighbouring EU states (or federal + regional). No real-time biometric identification in the pilot.

Duration: 10–12 weeks (engineering + legal + evaluation)

Phase	Duration	Activities / Deliverables
Prep & legal alignment	Wk 0–1	NDAs/MoU, DPIA/Fundamental-Rights assessment draft, permitted-fields catalogue
Baseline & adapters	Wks 1–3	Deploy Pulse clients on-prem; map local systems; configure policy engine
Federated model set-up	Wks 3–6	Configure secure aggregation, DP clipping/noise; train link-logic embeddings
Pilot run & inference	Wks 6–9	Run test set across cross-border case pairs; human-in-the-loop review
Audit & compliance report	Wks 9–11	Generate audit trails, explainability summaries, AI-Act conformity artefacts
Wrap & handover	Wk 12	Lessons learned; operations guide; rollback plan; SIENA/EIS/Prüm II roadmap

B) Architecture (Pulse as backbone; analytics as a layer above)

                    +-----------------------+
                    | Oversight & Regulator |
                    | Dashboard / Audit APIs|
                    +-----------+-----------+
                                |
                (read-only: logs, model cards, DPIA exports)
                                |
            +-------------------+--------------------+
            | Federated Coordinator / Orchestrator  |
            | (EU-cloud or sovereign host)          |
            +---+---------------------+-------------+
                |                     |
     secure aggregated updates   model registry, policy
                |                     |
     +----------+-----------+         |
     |                      |         |
Client Node A         Client Node B   … (many)
(agency on-prem)      (agency on-prem)

Each Client Node:
  • Policy engine (permitted fields/tasks)
  • Data adapters/ETL to local systems
  • Local training/inference (no raw PI leaves)
  • DP/secure aggregation wrapper
  • Attestation + signed checkpoints
  • Human-in-the-loop review UI (agency-owned)
  • Append-only audit logger

Important: "Analytics UI" is not Pulse; it's an agency/partner module that consumes Pulse-produced model artefacts and provenance under strict policy.

• Federated Coordinator: Orchestrates rounds, secure aggregation, model versioning
• Client Nodes: Stay inside each agency's network; train/infer locally
• Policy Engine & Audit: Enforce legal filters, log every material action
• Interoperability connectors: SIENA/EIS for secure info exchange
• Security & PETs: Secure aggregation, differential privacy, attestation

C) Vendor Comparison (Infrastructure vs. Analytics Suite)

Dimension	SYNNQ Pulse	Palantir Gotham/Foundry
Data sovereignty	Raw data stays local; federated updates only	Often centralised data fusion for analytics efficiency
Regulatory posture	Built to generate AI-Act/LED artefacts	Mature enterprise governance, but perceived opacity
Auditability	Append-only logs, model cards, oversight exports	Audits possible, but more vendor-controlled
EU sovereign stack fit	Backbone for agencies/partners to build analytics on top	Turnkey analytics, but with lock-in/sovereignty trade-offs

D) Execution Timeline (first 6–12 months)

Months 1–3 — Pilot

Deploy Pulse clients + coordinator; run non-biometric link-analysis pilot; produce DPIA/FRIA + logs; red-team & bias tests; no centralised raw PI.

Months 4–6 — Federated expansion

Scale to 3–5 nodes; start SIENA/EIS proof connectors; map Prüm II transaction semantics.

Months 7–9 — Hardening & optimisation

PETs tuning (DP budgets vs. utility), comms compression, drift/rollback drills; certify secure aggregation path.

Months 10–12 — Certification & rollout

Independent security/privacy audits; Member-State conformity steps for AI-Act high-risk deployments; publish transparency/oversight summaries.

Pilot Scope

LED & AI Act Compliant

This example demonstrates how SYNNQ Pulse processes metadata locally while maintaining compliance with the Law Enforcement Directive (LED), AI Act obligations for high-risk LEA systems, and EU police interoperability frameworks (SIENA/EIS/Prüm II).

Key Principles

No raw PI leaves the agency
Metadata-only processing
Federated learning with DP
Secure aggregation

Compliance

LED data minimisation
AI Act auditability
Purpose limitation
Retention controls

Example Local Data (Agency A)

Non-biometric case metadata typically allowed in a pilot. Identifiers are agency-scoped; no free-text content or biometrics.

{
  "case_id": "A-2025-01421",
  "subject_local_id": "A-SUBJ-7781",
  "event_type": "financial_transfer",
  "counterparty_hint": "B-ACCT-93312",
  "amount_eur": 4850.00,
  "timestamp_utc": "2025-08-31T10:12:03Z",
  "channel": "SEPA",
  "location_grid": "DE-BE-11000000",
  "device_hash": "dev_f1a2…",
  "comm_hash": "comm_7e9b…",
  "risk_tags": ["smurfing", "layering"],
  "legal_basis": "Art. 10 LED – prevention/detection of offences",
  "retention_class": "R-24M",
  "source_system": "fincrim_db"
}

Why metadata? It's fit-for-purpose and easier to justify under LED data minimisation & necessity; raw PI or content can remain local while still enabling link-analysis via derived features.

SYNNQ PULSEDEMO