Compliance and reporting automation

1) Why automate compliance

• Reduce manual errors and compliance costs.
• Transparency for auditors: traced artifacts, unchanging logs.
• Adapt quickly to rule changes.
• Built-in control in SDLC and operation (shift-left + shift-right).

2) Dictionary and frames

Controls: verifiable risk mitigation measures (preventive/detective/corrective).
Evidence/Evidence base: logs, reports, configuration dumps, screenshots, CI/CD artifacts.
GRC platform: register of risks, controls, requirements, tasks and audits.
Compliance-as-Code (CaC): policies/controls are described declaratively (YAML, Rego, OPA, Sentinel, etc.).
RegOps: operational fulfillment of requirements with SLOs/alerts, as a separate function.

3) Control map (reference matrix)

4) Automation architecture (reference)

1. Data sources: productive databases/logs, DWH/datalake, access systems, CI/CD, cloud configs, ticketing, mail/chats (archives).

2. Collection and normalization: connectors → the event bus (Kafka/Bus) and ETL/ELT in the Compliance showcases.

3. Rules and policies (CaC): policy repository (YAML/Rego), linters, review, versioning.

4. Detection and orchestration: rules engine (stream/batch), SOAR/GRC for tasks and escalations.

5. Reporting and evidence: regform generators, PDF/CSV, dashboards, WORM archive for immutability.

6. Interfaces: portals for Legal/Compliance/Audit, API for regulators (where available).

5) Data and event flows (example)

Access Governance: grant/revoke/role change events → extra privileges rule → remediation ticket → monthly attest report.
Retention/deletion: TTL/deletion events → control "out of sync with the policy →" alert + blocking by Legal Hold if necessary.
AML monitoring: transactions → rule engine and ML segmentation → cases (SAR) → uploading to the regulatory format.
Vulnerabilities/configurations: CI/CD → scanners → "hardening policy" → waivers report with expiration date.

6) Compliance-as-Code: How to describe policies

• Declarative format (policy-as-code) with clear inputs/outputs.
• Versioning + code review (PR) + changelog with reporting impact.
• Unit/property-based policy tests and sandbox environment for retro run.

yaml id: GDPR-Retention-001 title: "TTL for personal data - 24 months"
scope: ["db:users. pi", "s3://datalake/pi/"]
rule: "object. age_months <= 24          object. legal_hold == true"
evidence:
- query: retention_violations_last_24h. sql
- artifact: s3://evidence/retention/GDPR-Retention-001/dt={{ds}}
owners: ["DPO","DataPlatform"]
sla:
detect_minutes: 60 remediate_days: 7

7) Integrations and systems

GRC: register of requirements, controls, risks, owners, tasks and inspections.
IAM/IGA: role catalog, SoD rules, access review campaigns.
CI/CD: gate-plugins (quality/compliance gates), SAST/DAST/Secret scan, OSS licenses.
Cloud Security/IaC: Terraform/Kubernetes scan for policy compliance.
DLP/EDRM: sensitivity labels, auto-encryption, no exfiltration.
SIEM/SOAR: event correlation, control violation response playbooks.
Data Platform: "Compliance" showcases, lineage, data catalog, masking.

8) Regulatory reporting: typical cases

GDPR: Treatment Registry (Art. 30), incident reports (Art. 33/34), DSAR KPIs (timing/outcome).
AML: SAR/STR reports, trigger aggregates, case decision log, escalation evidence.
PCI DSS: scanning reports, network segmentation, inventory of systems with card data, key control.
SOC 2: control matrix, confirmation log, screenshots/configuration logs, control test results.

Formats: CSV/XBRL/XML/PDF, signed and saved in a WORM archive, with a hash summary.

9) Compliance metrics and SLOs

Coverage: percentage of systems with controls enabled.
MTTD/MTTR (controls): mean detection/remediation time.
False Positive Rate according to detective rules.
DSAR SLA:% closed on time; median response time.
Access Hygiene:% of obsolete rights; closing time of toxic combinations.
Drift: number of drifts per month.
Audit Readiness: time to collect evidence for audit (target: hours, not weeks).

10) Processes (SOP) - from reasoning to practice

1. Discovery & Mapping: data/system map, criticality, owners, regulatory bindings.
2. Design policy: formalization of → policy-as-code requirements → tests → reviews.
3. Implementation: deployment of rules (staging → prod), inclusion in CI/CD and event bus.
4. Monitoring: dashboards, alerts, weekly/monthly reports, control committee.
5. Remediation: automatic playbooks + tickets with deadlines and RACI.
6. Evidence & Audit: regular snapshot of artifacts; preparation for external audit.
7. Changes: policy versioning, migrations, deactivation of outdated controls.
8. Reassessment: quarterly performance review, rule tuning and SLO.

11) Roles and RACI

12) Dashboards (minimum set)

Compliance Heatmap: control coverage by system/business line.
SLA Center: DSAR/AML/SOC 2/PCI DSS deadlines, delinquencies.
Access & Secrets: "toxic" roles, expired secrets/certificates.
Retention & Deletion: TTL violations, freezes due to Legal Hold.
Incidents & Findings: trends of violations, repeatability, efficiency of remediation.

13) Checklists

Start automation program

• Register of requirements and risks agreed with Legal/Compliance.
• Assigned control owners and stakeholders (RACIs).
• Data connectors and Compliance are configured.
• Policies are described as code, covered by tests, added to CI/CD.
• Alerts and dashboards configured, SLO/SLA defined.
• The evidence snapshot process and WORM archive are described.

Before External Audit

• Updated control matrix ↔ requirements.
• A dry-run of evidence collection was conducted.
• Expired remediation tickets closed.
• Waivers with expiry dates updated.

14) Artifact patterns

Compliance Ops Weekly Report (Structure)

1. Summary: key risks/incidents/trends.
2. Metrics: Coverage, MTTD/MTTR, DSAR SLA, Drift.
3. Violations and correction status (by owner).
4. Policy changes (versions, impact).
5. Plan for the week: priority remediation, access review.

Inspection card (example)

ID/Name/Description

Standard (s )/Risks

Тип: Preventive/Detective/Corrective

Scope (systems/data)

Policy as Code (Link/Version)

Effect metrics (FPR/TPR)

Owner/Backup Owner

Evidence (what and where is stored)

Exceptions (who approved, before when)

15) Antipatterns

Compliance in Excel - no checks and traceability.
Manual reports "on demand" - no predictability and completeness.
Blind copying of requirements - without assessing risks and business context.
Monolith of rules - without versioning and tests.
Lack of operational feedback - metrics do not improve.

16) Maturity model (M0-M4)

M0 Manual: scattered practices, no dashboards.
M1 Catalogue: requirements and systems register, minimum reports.
M2 AutoTest: events/alerts, individual policies as code.
M3 Orchestrated: GRC + SOAR, scheduled reg reports, 80% controls in code.
M4 Continuous Assurance: continuous checks in SDLC/sales, auto-evidence, self-service auditors.

17) Security and privacy in automation

Minimizing data in Compliance cases.
Least privilege access, segmentation.
Immutable evidence archives (WORM/Object Lock).
Data Encryption and Key Discipline (KMS/HSM).
Logging and monitoring access to reports and artifacts.

18) Related wiki articles

Privacy by Design and Data Minimization

Legal Hold and Data Freeze

Data Retention and Deletion Schedules

DSAR: user requests for data

PCI DSS/SOC 2 Control and Certification

Incident management and forensics

Total

Compliance automation is systems engineering: policies as code, observability, orchestration, and evidence base. Success is measured by control coverage, reaction rate, reporting quality, and on-the-button audit readiness.