157dbbb9eb
## Phase 8: Enterprise Extractor Improvements ✅ - 14 security extractors (TLS, JWT, SQL injection, XSS, etc.) - 10 framework-specific extractors (Spring, Django, Rails, etc.) - Config file security detection (YAML, TOML) ## Phase 9: Autonomous Extractor Generation ✅ - Shadow mode executor with TP/FP tracking - Graduation pipeline with confidence thresholds - Auto-rollback on regression detection - Cross-project pattern syncing ## UAT Suite Complete (14 scripts, 90 tests) - test-core-detection.sh (6 tests) - test-declarative-extractors.sh (5 tests) - test-domain-frameworks.sh (5 tests) - test-domain-unreal.sh (3 tests) - test-llm-extraction.sh (6 tests) - test-eval-harness.sh (5 tests) - test-cross-language.sh (3 tests) - test-precommit-performance.sh (4 tests) - test-output-formats.sh (8 tests) - test-drift-detection.sh (6 tests) - test-exit-codes.sh (12 tests) + 3 more scripts ## Other Changes - Updated roadmap to mark Phase 8-9 complete - Added .gitignore entries for build artifacts - Updated pre-commit: 800 line limit, exclude tests/data/cmd Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
337 lines
12 KiB
HTML
337 lines
12 KiB
HTML
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<title>StemeDB - Assertions with Provenance</title>
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</head>
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<body>
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<div class="reveal">
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<div class="slides">
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<!-- Slide 1: The Problem (Lead with data) -->
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<section>
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<h2>In 2024, <span class="highlight">79%</span> of FDA Warning Letters<br>cited data integrity failures</h2>
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<div class="stat-block">
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<span class="number">85%</span>
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<span class="label">of safety and efficacy issues in Complete Response Letters<br>are never disclosed by companies.</span>
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</div>
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<p class="fragment fade-up muted" style="font-size: 0.8em; margin-top: 1.5em;">
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With AI making more decisions, the audit trail matters more than ever.
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</p>
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<aside class="notes">
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79% stat: FY2024 FDA Form 483 analysis - most common deficiency is lack of audit trails.
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85% stat: 2015 BMJ cross-sectional study on CRL disclosure gaps.
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Example: Feb 2025 Warning Letter to Exer Labs for AI diagnostic without QMS (inspection Oct 2024).
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</aside>
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</section>
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<!-- Slide 2: Why This Happens -->
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<section>
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<h2>Why this keeps happening</h2>
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<ul>
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<li class="fragment">Your data warehouse stores the <span class="highlight">current answer</span>. Sources disagree.</li>
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<li class="fragment">When a study is retracted, <span class="negative">which decisions did it affect?</span></li>
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<li class="fragment">AI recommended X. <span class="negative">Can you reconstruct why?</span></li>
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</ul>
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<p class="fragment muted" style="font-size: 0.75em; margin-top: 1.5em;">
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"Black box" is a documented rejection reason. Traditional databases overwrite history.
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</p>
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<aside class="notes">
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These aren't hypotheticals. The retraction question is what kills you in audits.
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"Can you reconstruct why" is the question every CMO dreads.
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510(k) submissions have grown to 1,000+ pages - FDA explicitly rejects "black box" AI.
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</aside>
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</section>
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<!-- Slide 3: Introducing StemeDB -->
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<section>
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<h1 style="font-size: 2.8em; font-weight: 600; letter-spacing: -0.03em;">StemeDB</h1>
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<p style="font-size: 1em; color: #a1a1aa; margin-top: 0.5em;">
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A knowledge graph that stores claims, not facts.
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</p>
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<p class="fragment muted" style="font-size: 0.75em; margin-top: 2em;">
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Append-only. Auditable. Built for regulated industries.
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</p>
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<aside class="notes">
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Quick intro - don't linger here. The next slide explains the approach.
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"Steme" from episteme (Greek for knowledge).
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</aside>
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</section>
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<!-- Slide 4: The Approach -->
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<section>
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<h2>Every claim has a source</h2>
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<p style="margin-bottom: 1em;">
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StemeDB stores <span class="highlight">assertions with provenance</span>, not overwritten facts.
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</p>
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<ul>
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<li class="fragment">When sources disagree, <span class="positive">you see the disagreement</span></li>
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<li class="fragment">When a source is retracted, <span class="positive">you know what's affected in seconds</span></li>
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<li class="fragment">History is preserved. Nothing gets silently overwritten.</li>
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</ul>
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<aside class="notes">
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This is the key insight. Version control for knowledge.
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Don't say "Git for Truth" - just describe the behavior.
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</aside>
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</section>
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<!-- Slide 4: Capabilities -->
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<section>
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<h2>What this enables</h2>
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<div class="capabilities-grid">
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<div class="capability-card">
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<h4>Conflict Visibility</h4>
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<p>See when sources disagree. Confidence scores show you how much.</p>
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</div>
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<div class="capability-card">
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<h4>Cascade Invalidation</h4>
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<p>Retract a source. See every downstream decision affected.</p>
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</div>
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<div class="capability-card">
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<h4>Complete Audit Trail</h4>
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<p>Every query logged with provenance. Export for regulators.</p>
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</div>
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</div>
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<p class="fragment muted" style="font-size: 0.7em; margin-top: 1.2em;">
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Time-travel queries: "What did we believe on January 1st?"
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</p>
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<aside class="notes">
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Keep this brief. The demo will prove these claims.
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Time-travel is the differentiator from every data warehouse.
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</aside>
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</section>
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<!-- Slide 5: Demo Preview -->
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<section class="transition-slide">
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<h2>Here's what it looks like</h2>
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<p class="muted" style="font-size: 0.7em; margin-bottom: 0.8em;">
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FDA guidance now requires audit trails for all AI-enabled devices. This is what compliance looks like.
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</p>
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<div class="demo-preview">
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<p style="font-size: 0.75em; color: #a1a1aa; margin: 0 0 0.8em 0;">Query:</p>
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<div class="query-preview">
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subject: <span style="color: #fbbf24;">semaglutide:gastroparesis_risk</span><br>
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predicate: <span style="color: #fbbf24;">risk_level</span>
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</div>
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<p style="font-size: 0.7em; color: #71717a; margin: 0.8em 0 0 0;">
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FDA labels say low incidence. Patient reports say otherwise.<br>
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Watch how StemeDB surfaces that conflict.
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</p>
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</div>
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<aside class="notes">
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"FDA has authorized over 1,200 AI-enabled devices. All require audit trails."
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"I'm going to run this exact query live. Watch what happens when sources disagree."
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Then switch to dashboard tab. Query should already be typed in.
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</aside>
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</section>
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<!-- Slide 6: Q&A (return here after demo) -->
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<section>
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<h2>Questions</h2>
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<div style="margin-top: 1.5em; text-align: left;">
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<p class="muted" style="font-size: 0.7em; margin-bottom: 0.8em;">What you saw:</p>
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<ul style="font-size: 0.75em;">
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<li><span class="highlight">Conflict visibility</span> — FDA vs patient reports, with confidence scores</li>
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<li><span class="highlight">Cascade invalidation</span> — Source retraction, instant impact assessment</li>
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<li><span class="highlight">Audit trail</span> — Every query logged, export-ready</li>
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<li><span class="highlight">Time-travel</span> — Point-in-time queries</li>
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<li><span class="highlight">Trust & Safety</span> — Quarantine queue, circuit breakers</li>
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</ul>
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</div>
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<aside class="notes">
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Be ready for:
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- "What's the latency?" → 340ms on 2.3M assertions
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- "SOC 2?" → In progress, not yet certified
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- "How do I get my data out?" → Standard export formats
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- "Who else uses this?" → Be honest about stage
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</aside>
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</section>
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</div>
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<div class="footer">
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StemeDB
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</div>
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</div>
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