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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>AI-Check — AI & LLM Security Governance Checklist</title>
<meta name="description" content="A comprehensive, interactive security and governance checklist for developers integrating AI and Large Language Models into production applications.">
<meta name="keywords" content="AI security checklist, LLM governance, prompt injection prevention, responsible AI, AI safety, EU AI Act compliance, LLM monitoring, AI risk assessment, LLMOps, AI audit">
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap" rel="stylesheet">
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
:root {
--bg: #ffffff;
--surface: #f8f8f9;
--surface-hover: #f0f0f2;
--border: rgba(0,0,0,0.06);
--border-strong: rgba(0,0,0,0.12);
--text: #111111;
--text-muted: #555555;
--text-dim: #999999;
--accent: #111111;
--accent-light: rgba(17,17,17,0.06);
--check: #22c55e;
--check-bg: rgba(34,197,94,0.08);
--severity-critical: #111;
--severity-high: #444;
--severity-medium: #777;
--severity-low: #aaa;
--radius: 12px;
--transition: 0.2s cubic-bezier(0.4, 0, 0.2, 1);
}
body {
font-family: 'Inter', -apple-system, system-ui, sans-serif;
background: var(--bg);
color: var(--text);
min-height: 100vh;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
/* ─── HERO ─── */
.hero {
position: relative;
padding: 80px 48px 56px;
max-width: 960px;
margin: 0 auto;
}
.hero-badge {
display: inline-flex;
align-items: center;
gap: 8px;
border: 1px solid var(--border-strong);
border-radius: 100px;
padding: 6px 16px;
font-size: 11px;
font-weight: 700;
letter-spacing: 0.1em;
text-transform: uppercase;
color: var(--text-muted);
margin-bottom: 28px;
}
.hero-badge::before {
content: '';
width: 6px;
height: 6px;
border-radius: 50%;
background: var(--text);
}
.hero h1 {
font-size: 44px;
font-weight: 900;
letter-spacing: -0.035em;
line-height: 1.05;
margin-bottom: 20px;
}
.hero-subtitle {
font-size: 16px;
font-weight: 400;
line-height: 1.7;
color: var(--text-muted);
max-width: 620px;
}
/* ─── PROGRESS BAR ─── */
.progress-container {
max-width: 960px;
margin: 0 auto 48px;
padding: 0 48px;
}
.progress-bar-wrap {
display: flex;
align-items: center;
gap: 16px;
}
.progress-track {
flex: 1;
height: 4px;
background: var(--surface);
border-radius: 4px;
overflow: hidden;
}
.progress-fill {
height: 100%;
width: 0%;
background: var(--text);
border-radius: 4px;
transition: width 0.4s cubic-bezier(0.4, 0, 0.2, 1);
}
.progress-label {
font-size: 13px;
font-weight: 700;
color: var(--text);
white-space: nowrap;
font-variant-numeric: tabular-nums;
}
/* ─── CONTENT ─── */
.content {
max-width: 960px;
margin: 0 auto;
padding: 0 48px 80px;
}
/* ─── CATEGORY ─── */
.category {
margin-bottom: 48px;
}
.category-header {
display: flex;
align-items: center;
gap: 12px;
margin-bottom: 20px;
padding-bottom: 14px;
border-bottom: 1px solid var(--border);
}
.category-number {
width: 28px;
height: 28px;
border-radius: 8px;
background: var(--text);
color: var(--bg);
display: flex;
align-items: center;
justify-content: center;
font-size: 12px;
font-weight: 800;
flex-shrink: 0;
}
.category-title {
font-size: 18px;
font-weight: 800;
letter-spacing: -0.01em;
}
.category-count {
font-size: 11px;
font-weight: 600;
color: var(--text-dim);
border: 1px solid var(--border);
padding: 3px 10px;
border-radius: 100px;
margin-left: auto;
}
/* ─── CHECKLIST ITEMS ─── */
.check-list {
display: flex;
flex-direction: column;
gap: 6px;
}
.check-item {
display: grid;
grid-template-columns: 40px 1fr auto;
align-items: start;
padding: 16px 20px;
border-radius: var(--radius);
border: 1px solid var(--border);
background: var(--bg);
transition: all var(--transition);
cursor: pointer;
user-select: none;
}
.check-item:hover {
background: var(--surface);
border-color: var(--border-strong);
}
.check-item.checked {
background: var(--check-bg);
border-color: rgba(34,197,94,0.15);
}
.check-item.checked .check-box {
background: var(--check);
border-color: var(--check);
}
.check-item.checked .check-box::after {
opacity: 1;
transform: scale(1);
}
.check-item.checked .item-title {
text-decoration: line-through;
color: var(--text-dim);
}
.check-box {
width: 20px;
height: 20px;
border: 2px solid var(--border-strong);
border-radius: 6px;
display: flex;
align-items: center;
justify-content: center;
transition: all var(--transition);
position: relative;
margin-top: 1px;
}
.check-box::after {
content: '';
width: 10px;
height: 6px;
border-left: 2px solid white;
border-bottom: 2px solid white;
transform: scale(0) rotate(-45deg);
transform-origin: center;
opacity: 0;
transition: all var(--transition);
position: absolute;
top: 3px;
}
.check-item.checked .check-box::after {
transform: scale(1) rotate(-45deg);
}
.item-content {
display: flex;
flex-direction: column;
gap: 4px;
}
.item-title {
font-size: 14px;
font-weight: 600;
line-height: 1.4;
transition: all var(--transition);
}
.item-why {
font-size: 13px;
font-weight: 400;
line-height: 1.55;
color: var(--text-dim);
}
.severity {
font-size: 10px;
font-weight: 700;
letter-spacing: 0.08em;
text-transform: uppercase;
padding: 4px 10px;
border-radius: 6px;
white-space: nowrap;
margin-top: 1px;
}
.severity.critical { background: var(--text); color: var(--bg); }
.severity.high { background: #e5e5e5; color: #333; }
.severity.medium { background: #f0f0f0; color: #666; }
.severity.low { background: #f8f8f8; color: #999; }
/* ─── FOOTER ─── */
.footer {
max-width: 960px;
margin: 0 auto;
padding: 32px 48px 56px;
border-top: 1px solid var(--border);
display: flex;
justify-content: space-between;
align-items: center;
}
.footer-text {
font-size: 13px;
color: var(--text-dim);
}
.footer-link {
font-size: 12px;
font-weight: 600;
color: var(--text);
text-decoration: none;
letter-spacing: 0.04em;
border-bottom: 1px solid var(--border-strong);
padding-bottom: 2px;
transition: opacity var(--transition);
}
.footer-link:hover { opacity: 0.6; }
/* ─── RESET BUTTON ─── */
.reset-wrap {
max-width: 960px;
margin: 0 auto;
padding: 0 48px 16px;
display: flex;
justify-content: flex-end;
}
.reset-btn {
background: none;
border: 1px solid var(--border);
border-radius: 8px;
padding: 6px 14px;
font-family: inherit;
font-size: 12px;
font-weight: 600;
color: var(--text-dim);
cursor: pointer;
transition: all var(--transition);
}
.reset-btn:hover {
border-color: var(--border-strong);
color: var(--text);
}
/* ─── RECOMMENDATION PANEL ─── */
.rec-toggle {
display: inline-flex;
align-items: center;
gap: 4px;
font-size: 11px;
font-weight: 600;
color: var(--text-dim);
cursor: pointer;
margin-top: 8px;
padding: 3px 0;
border: none;
background: none;
font-family: inherit;
transition: color var(--transition);
user-select: none;
}
.rec-toggle:hover { color: var(--text); }
.rec-toggle .arrow { transition: transform var(--transition); display: inline-block; font-size: 10px; }
.rec-toggle.open .arrow { transform: rotate(180deg); }
.rec-panel {
grid-column: 2 / -1;
max-height: 0;
overflow: hidden;
transition: max-height 0.35s cubic-bezier(0.4, 0, 0.2, 1), opacity 0.25s ease;
opacity: 0;
}
.rec-panel.open {
max-height: 600px;
opacity: 1;
}
.rec-inner {
padding: 12px 16px 4px;
margin-top: 8px;
border-top: 1px solid var(--border);
}
.rec-section-title {
font-size: 10px;
font-weight: 700;
letter-spacing: 0.1em;
text-transform: uppercase;
color: var(--text-dim);
margin-bottom: 6px;
}
.rec-steps {
list-style: none;
margin-bottom: 10px;
}
.rec-steps li {
font-size: 12.5px;
line-height: 1.55;
color: var(--text-muted);
padding-left: 16px;
position: relative;
margin-bottom: 3px;
}
.rec-steps li::before {
content: '→';
position: absolute;
left: 0;
color: var(--text-dim);
font-weight: 700;
}
.rec-tools {
display: flex;
flex-wrap: wrap;
gap: 6px;
margin-bottom: 10px;
}
.rec-tool {
font-size: 11px;
font-weight: 600;
padding: 3px 10px;
border-radius: 6px;
background: var(--surface);
border: 1px solid var(--border);
color: var(--text-muted);
text-decoration: none;
transition: all var(--transition);
}
.rec-tool:hover { border-color: var(--border-strong); color: var(--text); }
.rec-ref {
font-size: 11px;
color: var(--text-dim);
margin-bottom: 4px;
}
.rec-ref a { color: var(--text-muted); text-decoration: none; border-bottom: 1px solid var(--border); }
.rec-ref a:hover { color: var(--text); }
/* ─── RESPONSIVE ─── */
@media (max-width: 768px) {
.hero { padding: 48px 20px 36px; }
.hero h1 { font-size: 30px; }
.content, .progress-container, .footer, .reset-wrap { padding-left: 20px; padding-right: 20px; }
.check-item { grid-template-columns: 36px 1fr; gap: 4px; }
.severity { grid-column: 2; margin-top: 6px; width: fit-content; }
.rec-panel { grid-column: 2; }
.footer { flex-direction: column; gap: 12px; text-align: center; }
}
/* ─── PRINT ─── */
@media print {
.reset-wrap, .progress-container { display: none; }
.check-item { border: 1px solid #ddd; break-inside: avoid; }
.rec-panel { max-height: none; opacity: 1; }
}
</style>
</head>
<body>
<!-- ─── HERO ─── -->
<div class="hero">
<div class="hero-badge">AI & LLM Governance</div>
<h1>AI Integration<br>Security Checklist</h1>
<p class="hero-subtitle">A comprehensive governance and security checklist for developers shipping AI-powered features. Covers LLM security, privacy compliance, monitoring, cost management, and responsible deployment.</p>
</div>
<!-- ─── PROGRESS ─── -->
<div class="progress-container">
<div class="progress-bar-wrap">
<div class="progress-track"><div class="progress-fill" id="progressFill"></div></div>
<div class="progress-label" id="progressLabel">0 / 0</div>
</div>
</div>
<!-- ─── RESET ─── -->
<div class="reset-wrap">
<button class="reset-btn" onclick="resetAll()">Reset all</button>
</div>
<!-- ─── CONTENT ─── -->
<div class="content">
<!-- ━━━ 1. LLM SECURITY ━━━ -->
<div class="category">
<div class="category-header">
<div class="category-number">1</div>
<div class="category-title">LLM Security</div>
<span class="category-count">7 items</span>
</div>
<div class="check-list">
<div class="check-item" data-id="llm-1">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Prompt injection defenses in place</div>
<div class="item-why">System prompts are isolated from user input. Validation and guardrails block attempts to override model instructions, exfiltrate data, or hijack behavior.</div>
</div>
<div class="severity critical">Critical</div>
</div>
<div class="check-item" data-id="llm-2">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Jailbreak resistance tested</div>
<div class="item-why">The model is hardened against techniques that bypass its safety rails — including role-play attacks, encoding tricks, and multi-turn escalation.</div>
</div>
<div class="severity critical">Critical</div>
</div>
<div class="check-item" data-id="llm-3">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Hallucination mitigation strategy</div>
<div class="item-why">Responses are grounded in verified data (RAG). Temperature is tuned low. Outputs include source citations and confidence disclaimers where needed.</div>
</div>
<div class="severity critical">Critical</div>
</div>
<div class="check-item" data-id="llm-4">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Bias and fairness audits conducted</div>
<div class="item-why">Model outputs are regularly tested across demographics. Diverse test scenarios ensure the system does not discriminate by age, gender, ethnicity, or language.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="llm-5">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Content moderation filters active</div>
<div class="item-why">Input and output filters catch toxic, harmful, illegal, or off-topic content. Abuse reporting mechanisms are available to end users.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="llm-6">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Knowledge base integrity protected</div>
<div class="item-why">Write access to RAG sources is restricted. All changes are version-controlled. A review process prevents data poisoning of the knowledge base.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="llm-7">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Red-team testing performed</div>
<div class="item-why">Adversarial testing simulates real-world attacks including prompt injection, jailbreaking, and data extraction before every major release.</div>
</div>
<div class="severity high">High</div>
</div>
</div>
</div>
<!-- ━━━ 2. PRIVACY & COMPLIANCE ━━━ -->
<div class="category">
<div class="category-header">
<div class="category-number">2</div>
<div class="category-title">Privacy & Compliance</div>
<span class="category-count">6 items</span>
</div>
<div class="check-list">
<div class="check-item" data-id="priv-1">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">GDPR / data protection compliance</div>
<div class="item-why">No PII is stored without consent. Data processing agreements exist with all LLM providers. A processing register (Art. 30 GDPR) is maintained.</div>
</div>
<div class="severity critical">Critical</div>
</div>
<div class="check-item" data-id="priv-2">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Sensitive data handling policy</div>
<div class="item-why">Health, financial, or other special-category data has explicit consent flows, encryption at rest and in transit (TLS 1.2+), and access restrictions.</div>
</div>
<div class="severity critical">Critical</div>
</div>
<div class="check-item" data-id="priv-3">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">EU AI Act transparency requirements met</div>
<div class="item-why">Users are clearly informed they are interacting with an AI system. Documentation meets the forthcoming EU AI Act obligations for the system's risk tier.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="priv-4">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Copyright and liability safeguards</div>
<div class="item-why">Outputs are sourced from owned/licensed content only. Liability disclaimers are displayed. Legal review processes are in place for generated text.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="priv-5">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Audit logging implemented</div>
<div class="item-why">All API calls, errors, and model interactions are logged in a structured, PII-safe format. Log retention policies are defined. Central aggregation is set up.</div>
</div>
<div class="severity medium">Medium</div>
</div>
<div class="check-item" data-id="priv-6">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Data retention and deletion policy</div>
<div class="item-why">Conversation data has defined TTLs. Users can request deletion. Automated purge jobs ensure compliance with right-to-erasure requests.</div>
</div>
<div class="severity medium">Medium</div>
</div>
</div>
</div>
<!-- ━━━ 3. WEB SECURITY ━━━ -->
<div class="category">
<div class="category-header">
<div class="category-number">3</div>
<div class="category-title">Application Security</div>
<span class="category-count">5 items</span>
</div>
<div class="check-list">
<div class="check-item" data-id="web-1">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">XSS protection for AI outputs</div>
<div class="item-why">All model-generated content is HTML-escaped before rendering. CSP headers block inline scripts. Sanitization libraries handle markdown/HTML outputs.</div>
</div>
<div class="severity critical">Critical</div>
</div>
<div class="check-item" data-id="web-2">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">CSRF and SSRF protections active</div>
<div class="item-why">Anti-CSRF tokens protect state-changing requests. Outbound connections are restricted to allow-listed endpoints. No user-controlled URLs are fetched server-side.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="web-3">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Secure session management</div>
<div class="item-why">Sessions use cryptographically strong IDs, short TTLs, HttpOnly + Secure + SameSite cookies, and protection against session fixation.</div>
</div>
<div class="severity medium">Medium</div>
</div>
<div class="check-item" data-id="web-4">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Input validation on all AI-facing routes</div>
<div class="item-why">Request payloads are validated for size, type, and format before reaching the model. Malformed or oversized inputs are rejected early.</div>
</div>
<div class="severity medium">Medium</div>
</div>
<div class="check-item" data-id="web-5">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Zip bomb / decompression bomb defense</div>
<div class="item-why">Uploaded archives are validated before extraction. Limits on decompressed size, nesting depth, and file count prevent malicious archives from exhausting server memory, disk, or CPU.</div>
</div>
<div class="severity high">High</div>
</div>
</div>
</div>
<!-- ━━━ 4. INFRASTRUCTURE & OPS ━━━ -->
<div class="category">
<div class="category-header">
<div class="category-number">4</div>
<div class="category-title">Infrastructure & Operations</div>
<span class="category-count">5 items</span>
</div>
<div class="check-list">
<div class="check-item" data-id="infra-1">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">API keys and secrets secured</div>
<div class="item-why">No credentials in client-side code or public repos. Keys are stored in environment variables or secret managers. Regular rotation is automated.</div>
</div>
<div class="severity critical">Critical</div>
</div>
<div class="check-item" data-id="infra-2">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Rate limiting and DoS protection</div>
<div class="item-why">Per-IP and per-session rate limits are enforced. WAF or CDN edge protection is active. CAPTCHAs trigger on suspicious patterns.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="infra-3">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Availability and fallback strategy</div>
<div class="item-why">Graceful degradation when the LLM provider is down. Retry logic with exponential backoff. Health-check endpoints and status pages are available.</div>
</div>
<div class="severity medium">Medium</div>
</div>
<div class="check-item" data-id="infra-4">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Vendor lock-in mitigated</div>
<div class="item-why">An abstraction layer sits between the app and the LLM API. Alternative providers have been evaluated. SLAs and exit strategies are documented.</div>
</div>
<div class="severity medium">Medium</div>
</div>
<div class="check-item" data-id="infra-5">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Least-privilege IAM policies</div>
<div class="item-why">Service accounts use minimal permissions. Network segmentation isolates AI workloads. Access is audited and reviewed periodically.</div>
</div>
<div class="severity medium">Medium</div>
</div>
</div>
</div>
<!-- ━━━ 5. LLM MONITORING & OBSERVABILITY ━━━ -->
<div class="category">
<div class="category-header">
<div class="category-number">5</div>
<div class="category-title">LLM Monitoring & Observability</div>
<span class="category-count">5 items</span>
</div>
<div class="check-list">
<div class="check-item" data-id="mon-1">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Latency and throughput tracking</div>
<div class="item-why">P50/P95/P99 response times are monitored. Alerts fire when latency exceeds thresholds. Dashboards show real-time and historical trends.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="mon-2">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Token usage and cost monitoring</div>
<div class="item-why">Input/output token counts are logged per request. Daily and monthly spend is tracked. Anomaly detection catches unexpected usage spikes.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="mon-3">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Response quality scoring</div>
<div class="item-why">Automated evaluations score response relevance, groundedness, and safety. Feedback loops (thumbs up/down) provide continuous quality signals.</div>
</div>
<div class="severity medium">Medium</div>
</div>
<div class="check-item" data-id="mon-4">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Drift and anomaly detection</div>
<div class="item-why">Statistical monitoring detects shifts in model behavior, topic distribution, or refusal rates. Alerts trigger when patterns deviate from baselines.</div>
</div>
<div class="severity medium">Medium</div>
</div>
<div class="check-item" data-id="mon-5">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Error rate and failure mode tracking</div>
<div class="item-why">API errors, timeouts, and content filter rejections are tracked separately. Root cause dashboards help distinguish model issues from infra problems.</div>
</div>
<div class="severity medium">Medium</div>
</div>
</div>
</div>
<!-- ━━━ 6. MODEL GOVERNANCE ━━━ -->
<div class="category">
<div class="category-header">
<div class="category-number">6</div>
<div class="category-title">Model Governance</div>
<span class="category-count">5 items</span>
</div>
<div class="check-list">
<div class="check-item" data-id="gov-1">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Model versioning and changelog</div>
<div class="item-why">Every model swap or prompt change is version-tagged. A changelog documents what changed and why, enabling rollback to any previous state.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="gov-2">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Rollback plan documented and tested</div>
<div class="item-why">A one-click rollback to the previous model version is available. The process is documented and has been tested in a staging environment.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="gov-3">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Evaluation benchmarks defined</div>
<div class="item-why">A standardized test suite measures accuracy, safety, and task completion rates. Benchmarks run automatically before any model or prompt update ships.</div>
</div>
<div class="severity medium">Medium</div>
</div>
<div class="check-item" data-id="gov-4">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Model card or system documentation</div>
<div class="item-why">A model card documents the model used, its capabilities, known limitations, intended use cases, and ethical considerations.</div>
</div>
<div class="severity medium">Medium</div>
</div>
<div class="check-item" data-id="gov-5">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">A/B testing framework for model changes</div>
<div class="item-why">New models or prompt versions can be tested on a subset of traffic before full rollout, with quality metrics compared side by side.</div>
</div>
<div class="severity low">Low</div>
</div>
</div>
</div>
<!-- ━━━ 7. COST MANAGEMENT ━━━ -->
<div class="category">
<div class="category-header">
<div class="category-number">7</div>
<div class="category-title">Cost Management</div>
<span class="category-count">4 items</span>
</div>
<div class="check-list">
<div class="check-item" data-id="cost-1">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Budget caps and spending alerts</div>
<div class="item-why">Hard and soft spending limits are configured at the provider level. Alerts fire at 50%, 80%, and 100% of monthly budget thresholds.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="cost-2">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Per-request token budgets</div>
<div class="item-why">Max input and output token limits are enforced per request. Runaway conversations are capped. Context window usage is optimized.</div>
</div>
<div class="severity medium">Medium</div>
</div>
<div class="check-item" data-id="cost-3">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Cost attribution per feature</div>
<div class="item-why">Token usage is tagged by feature or endpoint, enabling granular cost visibility. Teams can identify which features drive disproportionate spend.</div>
</div>
<div class="severity medium">Medium</div>
</div>
<div class="check-item" data-id="cost-4">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Cost-efficiency optimization reviewed</div>
<div class="item-why">Prompt compression, caching, smaller models for simple tasks, and batch inference have been evaluated. Unit economics are documented.</div>
</div>
<div class="severity low">Low</div>
</div>
</div>
</div>
<!-- ━━━ 8. UX & RESPONSIBLE AI ━━━ -->
<div class="category">
<div class="category-header">
<div class="category-number">8</div>
<div class="category-title">UX & Responsible AI</div>
<span class="category-count">4 items</span>
</div>
<div class="check-list">
<div class="check-item" data-id="ux-1">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Human escalation paths defined</div>
<div class="item-why">Users with urgent or complex issues are seamlessly routed to human agents. The AI clearly communicates its limitations and offers alternatives.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="ux-2">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">AI disclosure visible to users</div>
<div class="item-why">Users are clearly informed they are interacting with AI, not a human. The disclosure is persistent and not hidden in fine print.</div>
</div>
<div class="severity high">High</div>
</div>
<div class="check-item" data-id="ux-3">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">User feedback mechanism in place</div>
<div class="item-why">Users can report inaccurate, offensive, or unhelpful responses. Feedback is routed to the team for continuous improvement.</div>
</div>
<div class="severity medium">Medium</div>
</div>
<div class="check-item" data-id="ux-4">
<div class="check-box"></div>
<div class="item-content">
<div class="item-title">Accessibility standards met</div>
<div class="item-why">The AI interface meets WCAG 2.1 AA. Screen readers can parse AI responses. Keyboard navigation works fully across the chat experience.</div>
</div>
<div class="severity medium">Medium</div>
</div>
</div>
</div>
</div>
<!-- ─── FOOTER ─── -->
<div class="footer">
<span class="footer-text">Last updated: March 2026 · Update this checklist with every major change to your AI integration.</span>
<a href="https://github.com/mhmdgazzar/AI-Check" class="footer-link">github.com/mhmdgazzar/AI-Check</a>
</div>
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// ─── RECOMMENDATIONS DATA ───
const RECS = {
'llm-1': {
steps: ['Deploy a prompt firewall that inspects all user inputs before they reach the model','Isolate system prompt from user-provided context using delimiter tokens or separate API calls','Apply input sanitization: strip control characters, limit length, reject known injection patterns','Run automated prompt injection test suites before each release'],
tools: [{n:'Vigil',u:'https://github.com/deadbits/vigil-llm'},{n:'LLM Guard',u:'https://github.com/protectai/llm-guard'},{n:'Rebuff',u:'https://github.com/protectai/rebuff'},{n:'Prompt Security',u:'https://prompt.security'}],
refs: ['OWASP LLM01:2025 — Prompt Injection','NIST AI 100-2e2025 — Adversarial ML']
},
'llm-2': {
steps: ['Test with known jailbreak datasets (DAN, AIM, role-play, encoding-based)','Deploy output classifiers that detect policy-violating responses','Implement multi-turn conversation analysis to catch escalation attacks','Use model providers\' built-in safety settings at maximum strictness'],
tools: [{n:'Garak',u:'https://github.com/leondz/garak'},{n:'Prompt Fuzzer',u:'https://github.com/prompt-security/ps-fuzz'},{n:'Lakera Guard',u:'https://lakera.ai'}],
refs: ['OWASP LLM01:2025 — Prompt Injection','MITRE ATLAS — AML.T0054 LLM Jailbreak']
},
'llm-3': {
steps: ['Ground all responses in a RAG pipeline with verified, versioned documents','Set temperature ≤ 0.3 for factual tasks; use 0.0 for structured extraction','Require the model to cite source documents in every response','Add confidence disclaimers: "This is AI-generated and may be inaccurate"'],
tools: [{n:'LlamaIndex',u:'https://llamaindex.ai'},{n:'LangChain',u:'https://langchain.com'},{n:'Vectara',u:'https://vectara.com'},{n:'Ragas',u:'https://ragas.io'}],
refs: ['OWASP LLM09:2025 — Misinformation']
},
'llm-4': {
steps: ['Run evaluation suites across demographic categories (age, gender, ethnicity, language)','Use automated bias detection tools to score output distributions','Incorporate diverse, representative test scenarios into CI/CD','Embed inclusive language directives in the system prompt'],
tools: [{n:'Fairlearn',u:'https://fairlearn.org'},{n:'AI Fairness 360',u:'https://github.com/Trusted-AI/AIF360'},{n:'DeepChecks',u:'https://deepchecks.com'}],
refs: ['NIST AI RMF — MAP 2.3 Bias Management','EU AI Act Art. 10 — Data Governance']
},
'llm-5': {
steps: ['Enable provider-level content filters (e.g., AWS Bedrock Guardrails, Azure Content Safety)','Add a secondary output classifier for toxicity, hate speech, and off-topic detection','Provide a visible "Report this response" button for end users','Log all filtered content for review and pattern analysis'],
tools: [{n:'OpenAI Moderation API',u:'https://platform.openai.com/docs/guides/moderation'},{n:'Guardrails AI',u:'https://guardrailsai.com'},{n:'TrustyAI',u:'https://github.com/trustyai-explainability'}],
refs: ['OWASP LLM05:2025 — Improper Output Handling']
},
'llm-6': {
steps: ['Restrict write access to the knowledge base to authorized personnel only','Track all document changes in version control (Git)','Require peer review before any knowledge base update goes live','Run automated integrity checksums on RAG source documents'],
tools: [{n:'Git',u:'https://git-scm.com'},{n:'DVC (Data Version Control)',u:'https://dvc.org'}],
refs: ['OWASP LLM04:2025 — Data and Model Poisoning','OWASP LLM08:2025 — Vector and Embedding Weaknesses']
},
'llm-7': {
steps: ['Schedule adversarial red-team sessions before every major release','Use automated fuzzing tools to test thousands of attack vectors','Include prompt injection, jailbreak, data exfiltration, and PII leak scenarios','Document all findings and track remediation in a security register'],
tools: [{n:'Garak',u:'https://github.com/leondz/garak'},{n:'Prompt Fuzzer',u:'https://github.com/prompt-security/ps-fuzz'},{n:'HuggingFace Red Team',u:'https://huggingface.co/blog/red-teaming'}],
refs: ['NIST AI 100-2e2025 — Adversarial ML Testing','MITRE ATLAS Framework']
},
'priv-1': {
steps: ['Execute a Data Processing Agreement (DPA) with every LLM provider','Maintain a processing register per Art. 30 GDPR','Implement PII detection and redaction before data reaches the LLM','Display privacy policy and consent flows before first interaction'],
tools: [{n:'Microsoft Presidio',u:'https://github.com/microsoft/presidio'},{n:'LLM Guard PII Scanner',u:'https://github.com/protectai/llm-guard'},{n:'OneTrust',u:'https://onetrust.com'}],