Requirements & audience:
- Executive + data platform leadership need a single-pane view of security posture, trends, risk hotspots, and ROI signals for remediation/investment decisions. Must be actionable, data-driven, and trustworthy.
High-level dashboard layout:
- Executive summary: current risk score (0–100), trend, top 3 risks, SLA/SLO health.
- Key metrics panel (real-time / 30‑90d trend).
- Drilldowns: sensitive data estate, access anomalies, detection/containment timelines, policy drift by service.
- Recommendations & suggested investments with estimated risk reduction.
Core metrics & SLOs (suggested targets):
- Mean Time To Detect (MTTD): median 30 minutes; 95th percentile < 4 hours.
- Mean Time To Contain (MTTC): median 2 hours; 95th percentile < 24 hours.
- Unauthorized Access Rate: unauthorized sessions / total sessions; SLO < 0.01%.
- Percentage Sensitive Data Discovered & Classified: discovered / expected sensitive objects; SLO > 95%.
- Policy Drift Rate: number of policy deviations per week per service; SLO < 0.5.
- Privileged Account Changes Without Review: changes / total privileged changes; SLO < 0.5%.
- Vulnerability Remediation Time (critical): median < 7 days.
- Data Exfiltration Score: anomaly score from DLP models; threshold-based alerts.
Data collection & validation:
- Sources: Cloud IAM logs, audit trails (S3/GCS/Azure Blob), DB audit logs, DLP/classification outputs, SIEM/EDR, orchestration logs.
- Ingestion: centralized event pipeline (Kafka/pub-sub) → normalization layer → feature store for metrics.
- Validation: schema checks, golden-record sampling, checksum counts, duplicate detection, and automated anomaly detection on metric volume. Use periodic reconciliations: ingest counts vs source counts, and synthetic transactions (canary events) to validate detection pipelines.
Metric calculation notes:
- MTTD/MTTC: use correlated incident timelines from SIEM incidents and ticketing system; anchor to event timestamps to avoid human-reporting bias.
- Unauthorized access: combine policy-evaluator decisions + anomaly models to reduce false positives.
- Sensitive data coverage: mapping between data catalog (expected schemas) and scanner results; flag unmapped datasets.
Alert thresholds & routing:
- Severity levels: P0 (immediate): MTTC 95th > 24h OR confirmed exfiltration; P1: MTTD 95th > 8h OR Unauthorized Access Rate > 0.05% sustained; P2: Policy Drift weekly > 1%.
- Alerts: push to incident channel + create ticket with context & playbook. Include remediation owners (platform, infra, app teams).
- Alert tuning: require baseline burn-in (30 days) then adaptive thresholds using rolling percentiles.
How metrics inform governance & investment:
- Monthly governance review: trend-driven decisions. Example rules:
- If Sensitive Data Coverage < 95% → prioritize investment in discovery/classification tools and staff.
- If MTTC/P95 rising → invest in automation (containment playbooks, runbooks, automated quarantine).
- If Unauthorized Access Rate concentrated in few services → invest in IAM hardening & least-privilege projects for those services.
- Use cost-risk model: estimate expected annualized loss reduction per dollar of investment using observed incident frequency × impact; prioritize projects with highest ROI.
- Policy: require service owners to maintain Policy Drift < SLO; failure triggers remediation sprint and funding reallocation.
- KPIs for teams: tie part of platform roadmap to security SLO improvements and track in quarterly planning.
Governance & trust:
- Provide metric lineage and confidence score per metric; surface raw events for audit.
- Implement blameless postmortems for SLO breaches and feed fixes back into dashboard (closed-loop).
- Quarterly executive reports with high-confidence trends, outlier incidents, and recommended investments with ROI estimates.
This design ensures the dashboard is measurable, actionable, and directly linked to governance decisions and investment prioritization.
