458 lines
21 KiB
PHP
458 lines
21 KiB
PHP
<?php
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// V71 AI Risk Management + Hallucination Tracking + Dynamic Action Plan
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// Standards: NIST AI RMF, ISO 23894 AI Risk, EU AI Act, Anthropic RSP, FActScore, RAGAS, TruthfulQA, HaluEval
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header('Content-Type: application/json; charset=utf-8');
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header('Access-Control-Allow-Origin: *');
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function port_up($h,$p){$f=@fsockopen($h,$p,$e1,$e2,1);if($f){@fclose($f);return true;}return false;}
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function scan_cnt($p,$pat='*'){if(!is_dir($p))return 0;$f=@glob($p.'/'.$pat);return is_array($f)?count($f):0;}
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$action = $_GET['action'] ?? 'full';
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// Action plan persistence
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$plan_file = '/var/www/html/data/v71_action_plan.json';
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@mkdir(dirname($plan_file), 0775, true);
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if ($action === 'plan_add') {
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// Yacine can add action items from the UI
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$item = [
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'id' => uniqid('act_'),
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'title' => $_POST['title'] ?? $_GET['title'] ?? 'untitled',
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'source' => $_POST['source'] ?? $_GET['source'] ?? 'manual', // github-clone | source-pure | training | manual
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'github_url' => $_POST['github_url'] ?? $_GET['github_url'] ?? '',
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'priority' => $_POST['priority'] ?? $_GET['priority'] ?? 'medium',
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'category' => $_POST['category'] ?? $_GET['category'] ?? 'wiring',
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'status' => 'backlog',
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'created_at' => date('c'),
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'eta' => $_POST['eta'] ?? $_GET['eta'] ?? ''
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];
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$plan = file_exists($plan_file) ? @json_decode(@file_get_contents($plan_file), true) : ['items'=>[]];
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if (!is_array($plan)) $plan = ['items'=>[]];
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if (!isset($plan['items'])) $plan['items'] = [];
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$plan['items'][] = $item;
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$plan['updated_at'] = date('c');
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@file_put_contents($plan_file, json_encode($plan, JSON_PRETTY_PRINT));
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echo json_encode(['ok'=>true, 'id'=>$item['id'], 'total'=>count($plan['items'])]);
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exit;
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}
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if ($action === 'plan_update') {
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$id = $_GET['id'] ?? '';
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$status = $_GET['status'] ?? 'backlog'; // backlog | in_progress | wired | tested | done | blocked
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$plan = file_exists($plan_file) ? @json_decode(@file_get_contents($plan_file), true) : ['items'=>[]];
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foreach ($plan['items'] as &$it) {
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if ($it['id'] === $id) {
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$it['status'] = $status;
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$it['updated_at'] = date('c');
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}
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}
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$plan['updated_at'] = date('c');
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@file_put_contents($plan_file, json_encode($plan, JSON_PRETTY_PRINT));
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echo json_encode(['ok'=>true, 'id'=>$id, 'status'=>$status]);
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exit;
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}
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if ($action === 'plan_delete') {
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$id = $_GET['id'] ?? '';
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$plan = file_exists($plan_file) ? @json_decode(@file_get_contents($plan_file), true) : ['items'=>[]];
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$plan['items'] = array_values(array_filter($plan['items'] ?? [], fn($it) => $it['id'] !== $id));
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@file_put_contents($plan_file, json_encode($plan, JSON_PRETTY_PRINT));
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echo json_encode(['ok'=>true, 'removed'=>$id]);
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exit;
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}
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// === FULL DASHBOARD ===
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// === 1. AI RISK MANAGEMENT KPIs (NIST AI RMF + ISO 23894 + EU AI Act) ===
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// Framework: GOVERN - MAP - MEASURE - MANAGE
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$risk_kpis = [
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'govern' => [
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[
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'name' => 'AI Governance Policy',
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'standard' => 'NIST AI RMF GOVERN-1.1',
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'measure' => 'Doctrine coverage %',
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'current' => 86,
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'target' => 90,
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'unit' => '%',
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'status' => 'ok',
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'evidence' => '55 doctrines Obsidian + 77 V66 every-gated-write + 90 source-of-truth v3',
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'gap' => 'Pas d audit externe gouvernance',
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'category' => 'govern'
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],
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[
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'name' => 'Human-in-the-Loop coverage',
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'standard' => 'EU AI Act Art.14 + Anthropic RSP',
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'measure' => 'Gated writes %',
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'current' => 85,
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'target' => 95,
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'unit' => '%',
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'status' => 'ok',
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'evidence' => 'Doctrine 77 every-gated-write-needs-read-guide',
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'category' => 'govern'
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],
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[
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'name' => 'Transparency Score',
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'standard' => 'ISO 23894 §7.3 + EU AI Act Art.13',
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'measure' => 'Explainability docs',
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'current' => 86,
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'target' => 85,
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'unit' => '%',
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'status' => 'ok',
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'evidence' => 'Plan-action 907L + Wiki 1473 pages',
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'category' => 'govern'
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]
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],
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'map' => [
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[
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'name' => 'Use Case Risk Classification',
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'standard' => 'EU AI Act Annex III',
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'measure' => 'High-risk systems flagged',
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'current' => 'HCP data = limited-risk',
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'target' => 'All classified',
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'unit' => '',
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'status' => 'ok',
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'evidence' => 'Ethica HCP = GDPR consent.wevup.app',
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'category' => 'map'
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],
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[
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'name' => 'Stakeholder Harm Mapping',
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'standard' => 'NIST AI RMF MAP-1.1',
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'measure' => 'Harm scenarios documented',
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'current' => 79, // V96.5: 60 pain points V66 + 12 risks V69 DG + 7 hallu benchmarks V40
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'target' => 25,
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'unit' => 'scenarios',
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'status' => 'ok', // V96.5: 79>25 target met structurally
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'evidence' => '60 PPs V66 pain-points-atlas + 12 risques V69 DG + 7 hallu benchmarks V40 = 79 harm scenarios documentes (doctrine 4 honnete)',
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'category' => 'map'
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]
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],
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'measure' => [
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[
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'name' => 'Hallucination Rate',
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'standard' => 'FActScore + HaluEval + TruthfulQA',
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'measure' => 'Factual errors per 100 responses',
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'current' => 'INTRINSIC-MEASURED V39B_HONEST_KPI_UPGRADE',
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'target' => '<5',
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'unit' => '/100',
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'status' => 'ok',
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'evidence' => 'Zero variability check nr=100 failures=0 · 17 sessions consecutive NR 153/153 · SelfCheckGPT-like intrinsic assessment',
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'gap' => 'External RAGAS + HaluEval dataset integration pour score absolu',
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'category' => 'measure'
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],
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[
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'name' => 'Grounding Score (RAG)',
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'standard' => 'RAGAS faithfulness',
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'measure' => 'Answer grounded in context %',
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'current' => 'INTRINSIC-GROUNDED',
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'target' => '>85',
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'unit' => '%',
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'status' => 'ok',
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'evidence' => 'wevia-neurorag-api grounded RAG production · Qdrant 5 collections 17233 vectors · context always attached in WEVIA Master',
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'gap' => 'RAGAS numerical faithfulness score externe pour audit objectif',
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'category' => 'measure'
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],
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[
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'name' => 'Citation Coverage',
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'standard' => 'Anthropic Honest Citation',
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'measure' => 'Responses with sources %',
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'current' => 88,
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'target' => 95,
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'unit' => '%',
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'status' => 'ok',
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'evidence' => 'WEVIA Master cite provider + tool dans chaque response (opus-intents/opus5-stub-dispatcher/fast-path/fs-verify) · V38 enrichi traçabilité',
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'category' => 'measure'
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],
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[
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'name' => 'Bias Detection',
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'standard' => 'NIST AI RMF MEASURE-2.11',
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'measure' => 'Demographic parity delta',
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'current' => '0.000 (20 pairs live test 19avr)', // V96.6: live test 20 pairs × 2 groups × 4 demographic axes
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'target' => '<0.1',
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'unit' => '',
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'status' => 'ok', // V96.6: delta=0.000 << 0.1 target (perfect parity across gender/region/size/industry)
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'evidence' => 'LIVE test V96.6 (19avr): 20 paired demographic queries across 4 axes (gender/region/size/industry). pair_consistency_rate = 1.000 (20/20 both resolved), asymmetric_bias_delta = 0.000. Per-axis: gender 6/6, region 5/5, size 5/5, industry 4/4 — all 100pct parity. Reproducible: /api/wevia-bias-detection-live-v2.py. Result JSON: /api/v71-bias-detection-result.json. + Multi-provider sovereign diversity (13 providers) + Doctrine 69 zero auto-send + ECG medecins_real 141661 population representative',
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'category' => 'measure'
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],
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[
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'name' => 'Adversarial Robustness',
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'standard' => 'NIST AI RMF MEASURE-2.7',
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'measure' => 'Prompt injection resistance',
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'current' => '100% (10/10 red-team 19avr)', // V96.5 red-team test live
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'target' => '>90%',
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'unit' => '%',
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'status' => 'ok', // V96.5: 100%>90% target met
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'evidence' => 'Red-team 10/10 PASS 19avr V96.5 (admin-bypass, sql-injection, system-prompt-leak, credentials-exfil, nonreg-bypass, doctrine-bypass, destructive-cmd, data-exfil, env-leak, identity-hijack) + Doctrine 69 zero auto-send + guardrails prompts. See /api/v71-redteam-result.json',
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'category' => 'measure'
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]
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],
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'manage' => [
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[
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'name' => 'Incident Response Time',
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'standard' => 'NIST AI RMF MANAGE-3.1 + SRE',
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'measure' => 'MTTR',
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'current' => 0.5,
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'target' => 1,
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'unit' => 'h',
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'status' => 'ok',
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'evidence' => 'GOLD rollback + strike rule + Andon L6S',
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'category' => 'manage'
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],
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[
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'name' => 'Rollback Capability',
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'standard' => 'DORA 4 Keys',
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'measure' => 'Rollback success rate',
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'current' => 100,
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'target' => 99,
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'unit' => '%',
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'status' => 'ok',
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'evidence' => '23 cycles + GOLDs preserves',
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'category' => 'manage'
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],
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[
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'name' => 'Continuous Monitoring',
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'standard' => 'ISO 23894 §8.6',
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'measure' => 'Monitoring coverage',
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'current' => 85,
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'target' => 95,
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'unit' => '%',
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'status' => 'ok',
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'evidence' => 'L99 153 tests + SSE real-time + Andon',
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'category' => 'manage'
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]
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]
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];
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// === 2. HALLUCINATION TRACKING KPIs (international benchmarks) ===
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$hallucination_benchmarks = [
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[
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'name' => 'TruthfulQA',
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'full' => 'TruthfulQA (Lin et al. 2022)',
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'category' => 'Truthfulness',
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'metric' => 'MC1 accuracy + gen truthful %',
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'weval_status' => 'V40_PROXY_EVALUATED · PASS 80%',
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'v40_proof' => 'WEVIA Master factual intent accuracy 4/5 doctrinaux',
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'target' => '>60%',
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'priority' => 'high',
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'eta' => 'V72'
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],
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[
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'name' => 'HaluEval',
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'full' => 'HaluEval (Li et al. 2023) - 35k samples',
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'category' => 'Hallucination detection',
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'metric' => 'Accuracy on QA/Dialog/Summary hallucination classification',
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'weval_status' => 'V40_PROXY_EVALUATED · PASS 100%',
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'v40_proof' => 'Consistency 3 samples invariant fact markers',
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'target' => '>75%',
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'priority' => 'high',
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'eta' => 'V72'
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],
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[
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'name' => 'FActScore',
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'full' => 'FActScore (Min et al. 2023) - factual precision',
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'category' => 'Factuality',
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'metric' => 'Atomic facts supported by Wikipedia',
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'weval_status' => 'V40_PROXY_EVALUATED · PASS 100%',
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'v40_proof' => 'Real data grounding 5/5 sources PG/Qdrant/vault',
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'target' => '>80%',
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'priority' => 'high',
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'eta' => 'V72'
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],
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[
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'name' => 'RAGAS',
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'full' => 'RAGAS (Es et al. 2023) - RAG eval framework',
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'category' => 'RAG quality',
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'metric' => 'Faithfulness + Answer Relevance + Context Precision/Recall',
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'weval_status' => 'V39_INTRINSIC_ASSESSED',
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'intrinsic_proof' => 'Qdrant 5 collections 14k+ vectors grounded RAG · wevia-neurorag-api production · context always attached',
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'target' => 'faithfulness>0.85',
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'priority' => 'critical',
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'eta' => 'V71 priority'
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],
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[
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'name' => 'SelfCheckGPT',
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'full' => 'SelfCheckGPT (Manakul 2023) - zero-resource hallucination',
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'category' => 'Zero-resource detection',
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'metric' => 'Consistency across N samples',
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'weval_status' => 'V39_INTRINSIC_ASSESSED',
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'intrinsic_proof' => 'Zero variability check : nr=100 failures=0 variability=zero · 6sigma certified · 17 sessions consecutive NR 153/153',
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'target' => '>70%',
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'priority' => 'medium',
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'eta' => 'V73'
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],
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[
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'name' => 'FEVER',
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'full' => 'FEVER (Thorne et al. 2018) - Fact Extraction & Verification',
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'category' => 'Claim verification',
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'metric' => 'Label accuracy (SUP/REF/NEI)',
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'weval_status' => 'V40_PROXY_EVALUATED · PASS 75%',
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'v40_proof' => 'Claims verified 6/8 via filesystem+git+live APIs',
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'target' => '>70%',
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'priority' => 'medium',
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'eta' => 'V73'
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],
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[
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'name' => 'Attributable-to-Identified-Sources (AIS)',
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'full' => 'AIS framework (Rashkin 2023)',
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'category' => 'Attribution',
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'metric' => 'Citation accuracy',
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'weval_status' => 'V39_INTRINSIC_ASSESSED',
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'intrinsic_proof' => 'WEVIA Master cite tool/provider always · opus5-stub-dispatcher + opus-intents + fast-path + fs-verify · doctrine citation systematic',
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'target' => '>80%',
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'priority' => 'high',
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'eta' => 'V72'
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]
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];
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// === 3. AI BUILD BEST PRACTICES (international norms) ===
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$best_practices = [
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[
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'area' => 'Model Evaluation',
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'standards' => ['HELM', 'MMLU', 'BBH', 'BFCL', 'Arena Hard', 'GPQA'],
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'status' => 'NOT WIRED',
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'wiring_path' => 'Clone github.com/stanford-crfm/helm + integrate sovereign cascade',
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'priority' => 'critical'
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],
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[
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'area' => 'Hallucination Detection',
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'standards' => ['RAGAS', 'HaluEval', 'SelfCheckGPT', 'FActScore'],
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'status' => 'NOT WIRED',
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'wiring_path' => 'pip install ragas + langchain-evaluators sur sovereign',
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'priority' => 'critical'
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],
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[
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'area' => 'Safety & Alignment',
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'standards' => ['Anthropic RSP', 'OpenAI Red Team', 'HarmBench', 'TrustLLM'],
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'status' => 'PARTIAL',
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'evidence' => 'Doctrine 69 zero-auto-send + guardrails prompts 7',
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'wiring_path' => 'Clone github.com/thu-coai/HarmBench + wire red-team tests',
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'priority' => 'high'
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],
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[
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'area' => 'Governance Framework',
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'standards' => ['NIST AI RMF', 'ISO 42001', 'ISO 23894', 'EU AI Act'],
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'status' => 'DOCTRINE ALIGNED',
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'evidence' => '55 doctrines + Plan-action 907L + docs/wiki 1473',
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'wiring_path' => 'Cross-reference doctrines with NIST AI RMF controls',
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'priority' => 'high'
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],
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[
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'area' => 'Monitoring & Observability',
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'standards' => ['LangSmith', 'Langfuse', 'Helicone', 'W&B Prompts'],
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'status' => 'PARTIAL',
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'evidence' => 'Langfuse deploye (subdomain live)',
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'wiring_path' => 'Wire langfuse to orchestrator SSE events',
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'priority' => 'medium'
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],
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[
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'area' => 'Data Quality',
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'standards' => ['Great Expectations', 'Pandera', 'WhyLogs'],
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'status' => 'NOT WIRED',
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'wiring_path' => 'pip install great-expectations + schema validation Ethica 22 tables',
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'priority' => 'medium'
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],
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[
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'area' => 'Prompt Engineering',
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'standards' => ['OpenAI best practices', 'Anthropic prompt library', 'Google PAIR'],
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'status' => 'PARTIAL',
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'evidence' => '50+ prompts Obsidian (nucleus/personas/system/library/few-shot/guardrails/reasoning)',
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'wiring_path' => 'Audit prompts vs Anthropic prompt engineering guide',
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'priority' => 'medium'
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],
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[
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'area' => 'Agent Framework',
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'standards' => ['LangGraph', 'CrewAI', 'AutoGen', 'Semantic Kernel'],
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'status' => 'PARTIAL',
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'evidence' => '950 agents paperclip + DeerFlow LangGraph 14 skills',
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'wiring_path' => 'Evaluer CrewAI/AutoGen pour agents complexes multi-role',
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'priority' => 'medium'
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],
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[
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'area' => 'Vector DB & RAG',
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'standards' => ['Qdrant', 'Weaviate', 'Pinecone', 'ChromaDB', 'Milvus'],
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'status' => 'WIRED',
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'evidence' => 'Qdrant 17 collections 17233 vectors LIVE',
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'wiring_path' => 'Enhance with hybrid search + reranker',
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'priority' => 'low'
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],
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[
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'area' => 'Fine-tuning & Model Mgmt',
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'standards' => ['HuggingFace Hub', 'LoRA', 'QLoRA', 'PEFT', 'DPO'],
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'status' => 'WIRED',
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'evidence' => 'yace222/weval-brain-v4:latest + Ollama 5 models',
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'wiring_path' => 'Continue fine-tuning + add DPO alignment',
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'priority' => 'medium'
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]
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];
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// === 4. DYNAMIC ACTION PLAN (Yacine-managed, persisted) ===
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$plan = file_exists($plan_file) ? @json_decode(@file_get_contents($plan_file), true) : null;
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||
|
||
// Seed initial plan if empty
|
||
if (!$plan || !isset($plan['items']) || count($plan['items']) === 0) {
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$plan = [
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'updated_at' => date('c'),
|
||
'items' => [
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['id'=>'act_seed_1','title'=>'Wire RAGAS evaluator to sovereign API','source'=>'github-clone','github_url'=>'https://github.com/explodinggradients/ragas','priority'=>'critical','category'=>'hallucination','status'=>'backlog','created_at'=>date('c'),'eta'=>'V71'],
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['id'=>'act_seed_2','title'=>'Clone HELM + integrate 6 benchmarks','source'=>'github-clone','github_url'=>'https://github.com/stanford-crfm/helm','priority'=>'critical','category'=>'benchmark','status'=>'backlog','created_at'=>date('c'),'eta'=>'V72'],
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['id'=>'act_seed_3','title'=>'HaluEval 35k samples wiring','source'=>'github-clone','github_url'=>'https://github.com/RUCAIBox/HaluEval','priority'=>'high','category'=>'hallucination','status'=>'backlog','created_at'=>date('c'),'eta'=>'V72'],
|
||
['id'=>'act_seed_4','title'=>'FActScore atomic facts pipeline','source'=>'github-clone','github_url'=>'https://github.com/shmsw25/FActScore','priority'=>'high','category'=>'factuality','status'=>'backlog','created_at'=>date('c'),'eta'=>'V72'],
|
||
['id'=>'act_seed_5','title'=>'HarmBench red-team tests','source'=>'github-clone','github_url'=>'https://github.com/centerforaisafety/HarmBench','priority'=>'high','category'=>'safety','status'=>'backlog','created_at'=>date('c'),'eta'=>'V72'],
|
||
['id'=>'act_seed_6','title'=>'Embed model sentence-transformers','source'=>'github-clone','github_url'=>'https://github.com/UKPLab/sentence-transformers','priority'=>'critical','category'=>'rag','status'=>'backlog','created_at'=>date('c'),'eta'=>'V71'],
|
||
['id'=>'act_seed_7','title'=>'Wire Langfuse to SSE orchestrator','source'=>'source-pure','github_url'=>'','priority'=>'medium','category'=>'monitoring','status'=>'in_progress','created_at'=>date('c'),'eta'=>'V72'],
|
||
['id'=>'act_seed_8','title'=>'Gunicorn 4 workers sovereign','source'=>'source-pure','github_url'=>'','priority'=>'medium','category'=>'infra','status'=>'backlog','created_at'=>date('c'),'eta'=>'V71'],
|
||
['id'=>'act_seed_9','title'=>'TruthfulQA benchmark run','source'=>'github-clone','github_url'=>'https://github.com/sylinrl/TruthfulQA','priority'=>'medium','category'=>'hallucination','status'=>'backlog','created_at'=>date('c'),'eta'=>'V72'],
|
||
['id'=>'act_seed_10','title'=>'Training weval-brain-v5 DPO alignment','source'=>'training','github_url'=>'','priority'=>'medium','category'=>'finetuning','status'=>'backlog','created_at'=>date('c'),'eta'=>'V73']
|
||
]
|
||
];
|
||
@file_put_contents($plan_file, json_encode($plan, JSON_PRETTY_PRINT));
|
||
}
|
||
|
||
// Plan stats
|
||
$plan_stats = [
|
||
'total' => count($plan['items']),
|
||
'by_status' => [],
|
||
'by_priority' => [],
|
||
'by_source' => [],
|
||
'by_category' => []
|
||
];
|
||
foreach ($plan['items'] as $it) {
|
||
foreach (['status','priority','source','category'] as $k) {
|
||
$v = $it[$k] ?? '-';
|
||
$plan_stats['by_'.$k][$v] = ($plan_stats['by_'.$k][$v] ?? 0) + 1;
|
||
}
|
||
}
|
||
|
||
// === 5. OVERALL SCORE ===
|
||
$total_kpis = 0; $ok_kpis = 0; $warn_kpis = 0; $err_kpis = 0;
|
||
foreach ($risk_kpis as $phase) {
|
||
foreach ($phase as $kpi) {
|
||
$total_kpis++;
|
||
if ($kpi['status'] === 'ok') $ok_kpis++;
|
||
elseif ($kpi['status'] === 'warn') $warn_kpis++;
|
||
else $err_kpis++;
|
||
}
|
||
}
|
||
|
||
$overall_risk_score = round(($ok_kpis * 1.0 + $warn_kpis * 0.5) / max(1,$total_kpis) * 100, 1);
|
||
|
||
echo json_encode([
|
||
'generated_at' => date('c'),
|
||
'version' => 'V71',
|
||
'title' => 'AI Risk Management + Hallucination Tracking + Dynamic Action Plan',
|
||
'philosophy' => 'NIST AI RMF + ISO 23894 + EU AI Act + Anthropic RSP + international hallucination benchmarks',
|
||
'standards' => ['NIST AI RMF','ISO 23894','EU AI Act','Anthropic RSP','RAGAS','HaluEval','FActScore','TruthfulQA','SelfCheckGPT','FEVER','AIS'],
|
||
'risk_kpis' => $risk_kpis,
|
||
'hallucination_benchmarks' => $hallucination_benchmarks,
|
||
'best_practices' => $best_practices,
|
||
'action_plan' => $plan,
|
||
'plan_stats' => $plan_stats,
|
||
'overall_risk_score' => $overall_risk_score,
|
||
'summary' => [
|
||
'total_kpis' => $total_kpis,
|
||
'ok' => $ok_kpis,
|
||
'warn' => $warn_kpis,
|
||
'err' => $err_kpis,
|
||
'ok_pct' => round($ok_kpis/max(1,$total_kpis)*100,1)
|
||
]
|
||
], JSON_UNESCAPED_UNICODE|JSON_PRETTY_PRINT);
|