How Do You Validate AI for Inspection Report Analysis and Quality Trend Detection?

Aerospace manufacturers and MRO facilities generate thousands of inspection reports documenting findings on aircraft parts and assemblies. AI-powered natural language processing (NLP) can identify patterns and recurring problems that human reviewers might miss—but only if the AI correctly interprets technical language and doesn't hallucinate trends that don't exist.

Role: Quality Assurance Inspector
Organization Type: Aerospace Manufacturer / MRO
Domain: Quality Control & Safety Compliance

The Challenge

Inspection reports contain highly technical language, abbreviations, and domain-specific terminology that general-purpose NLP models may misinterpret. A single misclassified finding could mean the difference between catching a fleet-wide defect early and missing a safety-critical pattern until after an incident.

The challenge is ensuring AI systems can accurately parse technical inspection language, correctly identify true trends versus noise, and never minimize or dismiss safety-relevant findings.

Why Adversarial Testing Matters

Modern aviation AI systems—whether LLM-powered assistants, ML prediction models, or agentic workflows—are inherently vulnerable to adversarial inputs. These vulnerabilities are well-documented in industry frameworks:

• LLM01: Prompt Injection — Manipulating LLMs via crafted inputs can lead to misclassification of safety-critical inspection findings
• LLM02: Insecure Output Handling — Neglecting to validate NLP outputs may lead to incorrect trend analysis being acted upon
• LLM09: Overreliance — Failing to critically assess AI-identified trends can lead to missed defects or wasted investigations
• Subtle data manipulation — Ambiguous terminology or inconsistent reporting that causes NLP models to misclassify safety-critical findings

Industry Frameworks & Resources

This use case guide aligns with established AI security and risk management frameworks:

• OWASP Top 10 for LLM Applications — Industry-standard vulnerability classification for LLM systems
• NIST AI Risk Management Framework — Comprehensive guidance for managing AI risks across the lifecycle
• MITRE ATLAS — Adversarial Threat Landscape for AI Systems, providing tactics and techniques for AI security testing

The purpose of this use case guide is to:

1. Raise awareness of adversarial scenarios specific to this aviation application
2. Provide concrete suggestions for testing AI systems before deployment
3. Offer example adversarial prompts that can be used to evaluate AI agents or assistants being developed for this use case

The adversarial examples below are designed for security researchers, AI developers, and aviation professionals who need to validate that their AI systems behave safely under challenging conditions.

Context & Industry Requirements

Operational Constraints

• Inspection reports use inconsistent terminology across inspectors and facilities
• Legacy reports may be in different formats (handwritten, typed, digital)
• Time pressure to process reports and release parts back to service
• Multiple inspection stages (incoming, in-process, final) with different documentation standards

Regulatory Considerations

• AS9100/AS9110 quality management system compliance required
• FAA/EASA Part 21 (manufacturing) and Part 145 (repair) requirements
• Mandatory reporting of certain defect types to authorities
• Complete traceability from raw material to installed part

Safety Implications

• Missed defect patterns could affect hundreds of aircraft
• False positives waste investigation resources and delay production
• Incorrect trend analysis could misdirect corrective actions
• Quality escapes in aerospace can have catastrophic consequences

Data Quality Requirements

The AI system for this use case typically requires: