What Is a Deployment Clearance Report?

A Deployment Clearance Report (DCR) is the formal document that authorizes an AI system to move from evaluation to production. It's not a benchmark score or a technical evaluation summary—it's a clearance decision signed by stakeholders that says: "We have evidence this system is safe, effective, and ready for real users."

Think of it like a pharmaceutical submission to the FDA. Before a drug reaches patients, it goes through clinical trials, safety reviews, and a formal submission package. The DCR is the AI equivalent: the final authorization that says deployment is justified.

The DCR serves four critical functions:

42%
of organizations lack formal DCR-like processes, leading to deployment surprises
7-10 days
typical time required to produce a comprehensive DCR
15-20
typical number of stakeholders who review/sign a DCR
68%
of deployment issues caught at DCR review stage vs. post-deployment

The DCR is your safety net. It forces you to think through questions you'd otherwise skip: "What goes wrong if this fails?" "How will we know if performance degrades?" "Who decides to roll back?" These aren't theoretical exercises—they're the difference between a successful deployment and a crisis.

DCR Structure and Components

A comprehensive DCR has nine core sections, each serving a distinct purpose:

1. Title Page and Metadata

System name, version, evaluation date range, DCR author, stakeholder sign-offs, approval date. Include a one-line executive summary.

2. Executive Summary (1-2 pages)

Non-technical stakeholders read only this section. It must answer: Is this safe to deploy? Why? What are the biggest risks? This is the clearance decision in narrative form.

3. System Overview

What is the system? What does it do? Who uses it? How does it integrate with existing infrastructure? This section is for someone unfamiliar with your team's project.

4. Evaluation Summary

Structured presentation of eval results: metrics, baselines, comparisons to previous versions. Include graphs, tables, breakdowns by performance segment.

5. Risk Register

Comprehensive table of identified risks, severities, mitigation strategies, residual risk, and owners. This is the "what can go wrong" section.

6. Deployment Conditions

Explicit conditions under which deployment is approved. Monitoring thresholds. Rollback triggers. Escalation procedures. What must happen post-deployment for the system to stay live.

7. Safety and Fairness Analysis

Analysis of potential harms, bias, fairness issues, and mitigation strategies. Increasingly required for regulated AI.

8. Stakeholder Sign-Off

Explicit approval from key stakeholders: engineering lead, product lead, legal/compliance, privacy officer, business sponsor. Each stakeholder confirms they've reviewed and approve the deployment.

9. Appendices

Detailed eval methodology, raw data, rubrics, rater agreements, failure mode analysis, sensitivity analysis, assumptions.

Not all sections require equal depth. A simple internal tool might have a streamlined DCR. A high-stakes regulated application requires comprehensive treatment of each section. The structure above represents the most thorough approach.

The Executive Summary Section

The executive summary is the most important section. Here's why: most stakeholders won't read the full DCR. They'll read the summary and make a deployment decision based on that 2 pages. Make it count.

Structure of a Strong Executive Summary

Opening paragraph (what is this system?): Three sentences that a non-technical person can understand. System name, what it does, who uses it, business case.

Evaluation results paragraph (how well does it work?): Lead with the most important metric. For a customer service agent: "Task completion: 91% on test set, 87% on recent production data (3-day rolling window)." Include the baseline so readers understand if 91% is good. "Previous version: 76%. Improvement: +15 points."

Risk analysis paragraph (what could go wrong?): Highlight the top 3-5 risks. Be specific. Don't say "potential hallucinations." Say "In medical recommendation scenarios, 2.1% of responses contained unsupported medical claims. Mitigation: medical advisor review for first 50 recommendations per user."

Deployment conditions paragraph (what must be true for this to work?): State the exact conditions under which deployment is approved. Example: "Approved for production with the following conditions: (1) Real-time monitoring on hallucination rate, with alert at 3%+. (2) Manual escalation for any medical recommendation. (3) Rollback if task completion drops below 82% over any rolling 24-hour window."

Recommendation paragraph (should we deploy?): Explicit clearance decision. "We recommend approval for production deployment under the conditions above. The system is substantially improved over the baseline and the identified risks are mitigated through monitoring and human review."

Pro Tip

Write the executive summary last, after you've completed the detailed analysis. It's easier to summarize what you've found than to guess what findings you'll have. But front-load it in the document so busy stakeholders see it first.

Eval Results Section

This section presents evaluation data in a format that stakeholders can act on. It's not a research paper—it's a briefing for decision-makers.

Primary Metrics

Start with the one metric that matters most. For most AI systems, this is task success or outcome quality. Present it clearly:

Primary Metric: Task Completion Rate
  Test Set (curated benchmark):        91%
  Production Data (7-day window):      87%
  Baseline (previous version):         76%
  Target Threshold (for deployment):   85%
  Status:                              PASS ✓

Include error bars or confidence intervals if available. State the sample size. "91% on 2,340 test examples, 95% CI: [89.2%, 92.8%]."

Secondary Metrics by Performance Segment

Report metrics broken down by segments that matter: user type, query complexity, domain, language, etc. This is where hidden failures show up.

Segment Task Completion Hallucination Rate Avg Response Time Sample Size Overall 87% 2.1% 1.2s 2,340 Simple queries (<20 words) 94% 1.3% 0.8s 1,240 Complex queries (>50 words) 71% 4.2% 2.1s 680 Medical domain 82% 3.8% 1.5s 420 Technical domain 91% 1.2% 1.1s 890 Non-English queries 79% 3.1% 1.4s 280

This table immediately reveals: complex queries and medical domain are weak points. Non-English performance lags. A strong DCR calls out these gaps and explains mitigation strategies (e.g., "Medical queries will be escalated to human review for first 30 days").

Comparison to Baseline

Always compare to the previous version or a clear baseline. "87% completion" is meaningless without context. "87% vs. 76% baseline" tells the story.

Risk Register Format and Template

The risk register is the operational heart of the DCR. It documents every identified risk and how it's being managed. Here's the template:

RISK REGISTER TEMPLATE
================================================================================

Risk ID: MED-001
Category: Medical Accuracy
Title: System provides medical recommendations without sufficient evidence
Severity: CRITICAL (severity if it occurs)
Likelihood: MEDIUM (probability of occurrence)
Description: System can generate medical recommendations that lack sufficient
  supporting evidence or may be outdated. Could cause patient harm if users act
  on unsupported recommendations.

Mitigations:
  1. Medical domain eval includes accuracy validation against clinical guidelines
  2. Medical queries routed to human physician reviewer before delivery to user
  3. All medical recommendations include explicit "Not medical advice" disclaimer
  4. System is fine-tuned to refuse medical diagnosis requests (refuse rate: 96%)

Residual Risk: LOW
  After mitigations, risk of harm is substantially reduced. Human review catches
  most problematic recommendations. Disclaimer provides user warning.

Owner: [Name], Medical Safety Lead
Monitoring: Daily review of flagged medical recommendations
Escalation: If hallucination rate exceeds 3% in any day, escalate to CMO
Post-Deployment Review: Weekly for first month, then monthly

---

Risk ID: SCALE-001
Category: Performance Degradation
Title: Task completion accuracy drops below acceptable threshold at scale
Severity: HIGH
Likelihood: MEDIUM
Description: Current eval is on 2,340 examples. In production with 100K+ daily
  queries, latency or data shift could degrade performance. Complex queries
  (5% of traffic) drop to 71% accuracy—if this segment grows, overall accuracy
  drops below 85% threshold.

Mitigations:
  1. Real-time performance monitoring with 4-hour aggregation windows
  2. Automatic alert if task completion drops below 85% in any 4-hour window
  3. A/B testing: new version runs on 5% of traffic for 7 days before full rollout
  4. Rollback procedure documented and tested

Residual Risk: MEDIUM
  Real-time monitoring will catch degradation quickly. Rollback capability
  exists but takes 30-60 minutes to execute.

Owner: [Name], Deployment Engineer
Monitoring: Continuous, 4-hour windows
Escalation: Auto-alert if threshold breached; manual investigation within 2 hours
Post-Deployment Review: Twice daily for first week, then daily for month 2-3

A comprehensive risk register typically has 8-15 identified risks. For each, stakeholders clearly understand what can go wrong, how likely it is, what's being done to prevent it, and what happens if the mitigation fails.

Conditions of Deployment

Explicit deployment conditions are what separates a DCR from a research paper. This section states exactly what must be true for deployment to proceed, and what must remain true for the system to stay live.

Pre-Deployment Conditions (must all be true before going live)

  • Eval results reviewed and approved: All stakeholders have reviewed eval results and signed off that performance is acceptable.
  • Risk mitigations in place: All automated mitigations (monitoring alerts, safety checks, escalation workflows) are configured and tested.
  • Rollback plan tested: The documented rollback procedure has been executed in staging and confirmed to work.
  • Human review processes ready: All required manual review workflows (e.g., medical advisor escalation) are staffed and trained.
  • Monitoring dashboards deployed: All required real-time and batch monitoring is live and baseline metrics are recorded.
  • Documentation complete: User-facing documentation, support team training, and escalation contact list are finalized.

Post-Deployment Thresholds (must remain true for system to stay live)

MONITORING THRESHOLDS FOR PRODUCTION DEPLOYMENT
================================================================================

Metric: Task Completion Rate
  Threshold: Must stay above 85%
  Window: 4-hour rolling window
  Alert: Yellow alert if 85-88%, Red alert if below 85%
  Action: Investigate if yellow, prepare for rollback if red

Metric: Hallucination Rate (presence of unsupported claims)
  Threshold: Must stay below 4%
  Window: 8-hour rolling window
  Alert: Yellow if 3-4%, Red if 4%+
  Action: Immediate human review of flagged responses if red

Metric: Escalation Rate
  Threshold: Must not exceed 25% of queries (indicates system is deferring too much)
  Window: 24-hour rolling window
  Alert: Yellow if 20-25%, Red if 25%+
  Action: Investigate if queries are legitimately complex or system is broken

Metric: Response Latency (p95)
  Threshold: Must stay below 3 seconds
  Window: Hourly
  Alert: Yellow if 2.5-3s, Red if 3s+
  Action: Check infrastructure health; prepare for traffic shifting or rollback

Metric: User Satisfaction (CSAT from explicit feedback)
  Threshold: Must stay above 75% (where available)
  Window: Daily
  Alert: Yellow if 70-75%, Red if below 70%
  Action: Sample responses for quality degradation

Rollback Triggers

Explicit conditions that trigger automatic or manual rollback to previous version:

  • Automatic rollback: Task completion rate drops below 82% for any 2-hour window.
  • Manual rollback (recommended): Hallucination rate exceeds 5% in any 4-hour window.
  • Escalation review: Multiple threshold breaches in a 24-hour period warrant leadership review.
  • Incident-based rollback: If a production incident occurs (e.g., system makes high-stakes incorrect recommendation), immediate rollback.

Practice Exercise 1: Simple Chatbot DCR

You've built a customer support chatbot for a SaaS company. It answers product questions, troubleshoots basic issues, and escalates complex problems to human agents. Evaluation is complete. Now write a minimal DCR.

Evaluation Results Summary:

  • Task completion: 89% on test set (420 examples), 86% on 7-day production sample (1,200 queries)
  • Escalation accuracy: 94% (correctly identifies when to ask for human help)
  • Baseline (previous version): 76% task completion, 88% escalation accuracy
  • Eval dataset: 350 typical queries + 70 edge cases from support backlog
  • Identified risks: occasional factual errors about product features (2.3%), incorrect escalation on ambiguous requests (4% of queries)

Your task: Write a 2-page executive summary for this chatbot DCR. Include:

  1. Opening paragraph describing the system
  2. Eval results with baseline comparison
  3. Top 3 identified risks
  4. Mitigation strategies for each risk
  5. Post-deployment monitoring approach
  6. Clear deployment recommendation
Worked Example

Opening: "The Customer Support Chatbot is a conversational AI system designed to answer routine product questions and troubleshoot common technical issues for our SaaS platform. It currently handles ~200 support inquiries per day and is intended to reduce response time for standard questions from 4 hours (human agent) to <30 seconds (chatbot)."

Results: "Evaluation shows 86% task completion rate on recent production queries (improvement from 76% baseline). The system successfully escalates 94% of complex queries to human agents, preventing low-quality answers."

Risk 1: "Occasional factual errors about product features (2.3% of responses contain outdated or inaccurate product information). Mitigation: Chatbot trained on current product documentation; knowledge base updated monthly; false information rate monitored continuously."

Practice Exercise 2: High-Stakes Medical AI DCR

You've built an AI system that assists radiologists by flagging potential findings in chest X-rays. It's been evaluated extensively and shows promising results. The stakes are high: incorrect flagging could cause patient harm. Write a comprehensive risk register for this deployment.

System Description: Analyzes chest X-rays to identify regions of interest for potential findings (pneumonia, nodules, pneumothorax, etc.). Radiologist review is required for all system outputs—the system doesn't make final diagnoses.

Evaluation Results:

  • Sensitivity (detection of actual findings): 94% on test set of 2,100 X-rays
  • Specificity (avoiding false positives): 91%
  • False positive rate: 7% (system flags areas with no actual finding)
  • Evaluated on diverse patient population: age 18-92, multiple hospitals, multiple X-ray machines
  • Performance holds across subgroups: no significant performance drop by age group or hospital

Your task: Identify and document 5-8 risks for this medical AI system. For each risk:

  1. Give it a descriptive title and Risk ID (e.g., "RAD-001: False negative on subtle findings")
  2. Rate severity (CRITICAL / HIGH / MEDIUM / LOW) and likelihood (HIGH / MEDIUM / LOW)
  3. Describe the specific harm that could occur
  4. List 2-3 mitigation strategies
  5. Estimate residual risk after mitigations
  6. Identify the owner (what role/person is responsible for this risk?)
Example Risks

RAD-001: False Negatives on Subtle Findings - Severity: CRITICAL, Likelihood: MEDIUM. Description: Despite 94% sensitivity on test set, some subtle findings (early pneumonia, small nodules) may be missed, leading to delayed diagnosis. Mitigations: (1) Radiologist review mandatory for all scans (system is second reader, not first); (2) System accuracy validated monthly on new test cases; (3) Radiologists trained to interpret system confidence scores.

RAD-002: Demographic Disparity - Severity: HIGH, Likelihood: MEDIUM. Description: System may perform differently across demographic groups (age, sex, race) due to training data bias. Mitigations: (1) Eval broken down by age, sex, race demographics; (2) Prospective fairness monitoring; (3) Quarterly fairness audits.

Common DCR Mistakes

Mistake 1: Vague Risk Descriptions

Bad: "Risk of hallucination. Mitigation: careful evaluation and monitoring."

Good: "Risk ID: LLM-003: System generates citations to non-existent sources in legal contexts. Severity: CRITICAL. Likelihood: HIGH (occurs in ~8% of legal queries based on eval). Mitigation: (1) Legal domain flagged for mandatory human review, (2) All citations verified against actual legal databases before delivery, (3) Hallucination rate monitored per-domain."

Specificity matters. Vague risks aren't actionable.

Mistake 2: Missing Baseline Comparisons

Bad: "System achieves 87% task completion."

Good: "System achieves 87% task completion, up from 76% baseline (previous version). Improvement: +11 points. Threshold for deployment: 85%. Status: PASS."

Without a baseline, metrics are meaningless. Include previous version, human performance, or explicit minimum threshold.

Mistake 3: No Rollback Criteria

Bad: DCR approves deployment but doesn't define when to rollback. Later, performance degrades and stakeholders debate whether to roll back.

Good: "System will be automatically rolled back if task completion drops below 82% in any 2-hour window, without waiting for human review."

Rollback should be automatic for objective criteria. Don't leave this to opinion later.

Mistake 4: Ignoring Segment-Level Failure

Bad: Report 91% overall accuracy without breaking down by segment. Later discover it's only 62% accurate for Spanish-language queries.

Good: Report all metrics by language, user type, domain, and other relevant segments. Explain why gaps exist and how they'll be managed.

Mistake 5: No Monitoring Plan

Bad: "We'll monitor performance post-deployment." Vague. No specific metrics, windows, or alert thresholds.

Good: "Post-deployment monitoring: (1) Task completion rate via 4-hour rolling windows, alert if below 85%. (2) Hallucination rate via daily review of 100 random outputs, alert if above 3%. (3) Escalation rate monitored continuously, alert if above 25%. All alerts trigger incident review within 2 hours."

DCR Review Checklist

Before submitting your DCR for approval, work through this 20-item checklist. Each item should be addressed explicitly in the document.

Item Checklist Where Addressed 1. System purpose is clear Can a non-technical reader understand what the system does? System Overview, Executive Summary 2. Evaluation methodology documented How were metrics measured? What data? What raters? Eval Summary, Appendix 3. Primary metrics stated explicitly What is the most important measure of success? Eval Summary, Executive Summary 4. Baseline comparison included How does this version compare to previous version or threshold? Eval Summary 5. Segment-level breakdown Performance reported by language, domain, user type, etc. Eval Summary 6. Confidence intervals or uncertainty How confident are these results? What's the margin of error? Eval Summary, Appendix 7. Failure mode analysis What does the system get wrong? What patterns of failure? Risk Register, Appendix 8. Risk register complete All identified risks documented with severity, mitigation, owner Risk Register 9. Mitigation strategies specific Not vague; each mitigation is concrete and measurable Risk Register 10. Residual risk assessed After mitigations, what risk remains? Risk Register 11. Monitoring plan detailed What metrics, what windows, what alert thresholds post-deployment? Deployment Conditions 12. Rollback criteria defined Under what specific conditions is rollback triggered? Deployment Conditions 13. Rollback procedure tested Has the rollback plan been executed and verified to work? Pre-Deployment Conditions 14. Human review processes ready Are all escalation workflows staffed and trained? Pre-Deployment Conditions 15. Fairness and bias analysis Is performance equal across demographic groups? What disparities exist? Safety & Fairness Analysis 16. Privacy and security reviewed Does the system handle sensitive data appropriately? Safety & Fairness Analysis 17. Regulatory compliance addressed If applicable (medical, finance, etc.), are regulatory requirements met? Safety & Fairness Analysis 18. Stakeholder sign-offs obtained Engineering, Product, Legal, Privacy, Business all approve? Sign-Off Page 19. User documentation complete Do users understand limitations and how to escalate? Pre-Deployment Conditions 20. Clear deployment recommendation Explicit: approve deployment, approve with conditions, or disapprove? Executive Summary

A DCR that passes all 20 items is production-ready. If any items are missing or weak, the DCR isn't complete. Use this checklist as a quality gate before submitting for sign-off.

Key Insight

The DCR is not a marketing document. It's a risk management document. Its purpose is to catch problems before deployment, not to convince stakeholders the system is perfect. The best DCRs acknowledge weaknesses, describe mitigations, and set realistic expectations. Honesty about limitations increases stakeholder confidence, not decreases it.