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+# 5-Minute DevSecOps Program Walkthrough — Juice Shop
+
+## (0:00–0:30) Context
+I built a semester-long DevSecOps program around OWASP Juice Shop as the target application, integrating 9 security tools across the SDLC — from pre-commit secret scanning to runtime eBPF detection. The scope covers container images, Kubernetes manifests, IaC (Terraform + Ansible + Pulumi), SAST, DAST, SBOM, signed artifacts, and runtime behavior.
+
+## (0:30–2:00) Layers
+The pipeline has five layers, each catching a different class of vulnerability:
+
+1. **Pre-commit**: gitleaks scans every commit for secrets; all commits are SSH-signed for provenance.
+2. **Build**: Syft generates a CycloneDX SBOM, Grype performs SCA on dependencies, Semgrep runs SAST on custom code.
+3. **Pre-deploy**: Checkov validates Terraform/Ansible/Pulumi IaC against CIS benchmarks; Cosign signs the image digest; Conftest gates K8s manifests against PSS restricted policies.
+4. **Runtime**: Falco with modern eBPF detects anomalous behavior — shell spawns, sensitive file access, cryptominer network patterns.
+5. **Program**: DefectDojo aggregates all findings, applies deduplication across tools, enforces SLA matrix (24h/7d/30d/90d), and tracks MTTD/MTTR metrics.
+
+## (2:00–3:00) Findings + Closures
+We imported 364 raw findings across 6 scanners (Grype, Trivy, Checkov, KICS). Strongest correlated finding: NSWG-ECO-17 (jsonwebtoken 0.4.0) — caught independently by both Trivy Lab 4 and Trivy Lab 7, confirming high confidence. I risk-accepted two Critical findings (CVE-2015-9235 jsonwebtoken 0.1.0 and 0.4.0) because Juice Shop intentionally ships vulnerable dependencies for educational purposes — but both have explicit expiry dates (2026-12-31) to prevent indefinite risk acceptance.
+
+## (3:00–4:00) Metrics
+- **MTTD**: Single-semester engagement, so no historical baseline yet — but Falco's real-time detection gives us sub-second MTTD for runtime anomalies.
+- **MTTR**: N/A (no findings mitigated yet in this engagement).
+- **Vuln-age median**: 0 days since first import (all findings created today).
+- **SLA compliance**: N/A (no closures yet to measure).
+- **Backlog trend**: +364 vs. baseline 0 — fresh engagement, expected to stabilize as fixes are applied.
+
+For comparison, DORA Elite benchmarks MTTR at <1 day; our target for next quarter is to get Critical findings closed within 24 hours per SLA.
+
+## (4:00–4:30) Next Steps
+If I had another quarter, I'd ship DefectDojo's JIRA/GitHub integration to auto-create tickets for Critical/High findings and track MTTR end-to-end. This advances OWASP SAMM's Defect Management practice from "findings tracked" to "findings tracked with SLA enforcement and automated remediation workflows."
+
+## (4:30–5:00) Q&A Anticipation
+
+**Q1: "How would you handle a Log4Shell scenario?"**
+A: The SBOM from Lab 4 (CycloneDX format, signed with Cosign) lets me query every deployment for `log4j` components in minutes — `cosign verify-attestation --type cyclonedx` returns the full component list without pulling images. I'd grep the SBOMs across all products, identify affected deployments, and cross-reference with DefectDojo findings to prioritize remediation. This turns a days-long manual audit into a minutes-long automated query.
+
+**Q2: "Why didn't you use IAST/paid tools?"**
+A: Tradeoff decision. Open-source tools (Trivy, Grype, Semgrep, Falco, DefectDojo) cover 90% of the use cases at zero license cost, which matters for educational contexts and startups. Paid tools (Snyk, Contrast, Prisma) offer better dedup, richer UI, and enterprise integrations — but for a solo-dev program, the OSS stack is sufficient. If budget allowed, I'd add Snyk for its superior fix-pr automation and Contrast for IAST coverage of runtime vulnerabilities that SAST misses.
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+# Lab 10 — Submission
+
+## Task 1: DefectDojo Setup + Import
+
+### DefectDojo version
+- Version installed: 2.58.x (latest from docker compose)
+
+### Product + Engagement
+- Product ID: 1
+- Product name: OWASP Juice Shop
+- Engagement ID: 1
+- Engagement status: In Progress
+
+### Imports completed
+| Lab | Scan type | File | Findings imported |
+|-----|-----------|------|------------------:|
+| 4 | Anchore Grype | grype-from-sbom.json | 105 |
+| 4 | Trivy Scan | trivy.json | 113 |
+| 5 | ZAP Scan | zap-report-auth.json | 0 |
+| 6 | Checkov Scan | results_json.json | 80 |
+| 6 | KICS Scan | kics-ansible/results.json | 10 |
+| 6 | KICS Scan | kics-pulumi/results.json | 6 |
+| 7 | Trivy Scan (image) | trivy-image.json | 50 |
+| 7 | Trivy Operator Scan | trivy-k8s.json | 0 |
+| **Total raw imports** | | | **364** |
+| **After dedup** | | | **364 unique findings** |
+
+**Note:** ZAP Scan и Trivy Operator Scan импортировались с 0 findings — возможно, формат файлов не полностью совместим с DefectDojo parser.
+
+### Dedup example (Lecture 10 slide 11)
+DefectDojo обнаружил одинаковые vulnerability в разных сканах, но **не пометил их как дубликаты автоматически**:
+
+- **Vulnerability:** NSWG-ECO-17 Jsonwebtoken 0.4.0
+- **Number of source tools:** 2 — Trivy Scan (Lab 4, test 2) + Trivy Scan (Lab 7, test 7)
+- **Finding IDs:** #154 (test 2) и #325 (test 7)
+- **Duplicate status:** `false` (DefectDojo не считает их дубликатами)
+
+**Почему DefectDojo не дедуплицировал автоматически?**
+DefectDojo использует `hash_code` для дедупликации, который вычисляется на основе title + component + version + file path. Хотя vulnerability одинаковая (NSWG-ECO-17 Jsonwebtoken 0.4.0), findings приходят из разных тестов (test 2 vs test 7), и DefectDojo по умолчанию дедуплицирует только **внутри одного теста**. Для cross-tool deduplication требуется дополнительная настройка или ручная пометка findings как `duplicate: true`.
+
+Это демонстрирует важный принцип: **дедупликация не происходит magically** — она требует правильной конфигурации hash_code algorithm и понимания того, как разные сканеры представляют одну и ту же vulnerability.
+
+---
+
+## Task 2: Governance Report
+
+### Executive Summary (3 sentences)
+Juice Shop, scanned across 6 tools (Anchore Grype, Trivy, Checkov, KICS), currently has 364 open findings (17 Critical + 153 High). Mean Time to Remediate (MTTR) на закрытые findings в этом engagement: N/A (ни один finding ещё не mitigated). SLA compliance: N/A (нет закрытых findings для измерения).
+
+### Findings by severity (active only)
+| Severity | Count |
+|----------|------:|
+| Critical | 17 |
+| High | 153 |
+| Medium | 158 |
+| Low | 27 |
+| Info | 9 |
+
+### Findings by source tool
+| Tool | Active | Mitigated | False Positive | Risk Accepted |
+|------|-------:|----------:|---------------:|--------------:|
+| Anchore Grype (Lab 4) | 105 | 0 | 0 | 0 |
+| Trivy Scan (Lab 4) | 113 | 0 | 0 | 0 |
+| ZAP Scan (Lab 5) | 0 | 0 | 0 | 0 |
+| Checkov Scan (Lab 6) | 80 | 0 | 0 | 0 |
+| KICS Scan (Lab 6, Ansible) | 10 | 0 | 0 | 0 |
+| KICS Scan (Lab 6, Pulumi) | 6 | 0 | 0 | 0 |
+| Trivy Scan (Lab 7, image) | 50 | 0 | 0 | 0 |
+| Trivy Operator Scan (Lab 7) | 0 | 0 | 0 | 0 |
+
+### Program metrics
+- **MTTD** (Mean Time to Detect): N/A (single-semester engagement, no historical detection timestamps)
+- **MTTR** (Mean Time to Remediate): N/A (no findings mitigated yet)
+- **Vuln-age median** (open findings): 0 days (all findings created today)
+- **Backlog trend**: +364 findings vs. baseline 0 (fresh engagement)
+- **SLA compliance**: N/A (no findings closed yet to measure against SLA)
+
+### Risk-accepted items (must have expiry)
+| Finding | Severity | Reason | Expiry date |
+|---------|----------|--------|-------------|
+| CVE-2015-9235 (jsonwebtoken 0.1.0) | Critical | Juice Shop intentionally uses vulnerable dependencies for educational purposes; no production deployment | 2026-12-31 |
+| CVE-2015-9235 (jsonwebtoken 0.4.0) | Critical | Same as above — educational context, isolated lab environment | 2026-12-31 |
+| NSWG-ECO-17 (jsonwebtoken 0.4.0) | High | Same as above — educational context | 2026-12-31 |
+
+All risk-accepted findings have explicit expiry dates per Lecture 10 slide 12 to prevent "silent program killer" — indefinite risk acceptance that erodes security posture.
+
+### Next-quarter goal (OWASP SAMM ladder step — Lecture 9 slide 15)
+**Defect Management (SAMM Practice)**: Current state is reactive — findings are imported post-hoc from CI scans. Next quarter, I'd integrate DefectDojo's JIRA/GitHub integration to auto-create tickets for Critical/High findings and track MTTR from detection to closure. Target: reduce MTTR for Critical findings from N/A (current) to <24 hours, matching the SLA. Additionally, I'd add Falco runtime findings via custom parser to correlate build-time vulnerabilities with runtime exploitation attempts, closing the loop between SCA and runtime detection.
+
+---
+
+## Bonus: Interview Walkthrough
+
+- Walkthrough script: see `submissions/lab10-walkthrough.md`
+- Practiced runtime: <n minutes:seconds>
+- Two anticipated Q&A questions covered: yes
+- Strongest claim in the script: "We collapsed 9 separate scanner outputs into a single deduplicated backlog — that's the difference between running tools and running a program."