AI & Application Audit Services
Independent, evidence-based assessment of production AI systems, microservice architectures and data platforms, finding reliability gaps and compliance risks before they become incidents.
- Full enterprise assessment
- 8 wks
- Microservices in one engagement
- 88
- Engineering teams covered
- 13
- Smaller platform assessments
- 2 to 4 wks
The short answer
What does an AI and application audit cover?
Scope
What we assess
Application architecture audit
- Microservices Mapping: Complete inventory and dependency mapping of distributed service architectures
- Service Boundary Analysis: Identification of coupling issues, shared database anti-patterns, and boundary violations
- Data Flow Documentation: End-to-end data flow tracing across all service interactions with evidence at file and line level
- Integration Pattern Review: Assessment of API contracts, event-driven patterns, retry logic, and failure handling
Reliability and resilience review
- Error Handling Assessment: Systematic review of exception handling, dead-letter queues, and failure recovery across all services
- Circuit Breaker & Retry Audit: Identification of absent or misconfigured circuit breakers creating silent failure risks
- Queue Resilience Analysis: Redis, Kafka, and message queue depth, timeout, and drain-time risk assessment
- Race Condition Identification: Multi-writer database patterns, transaction coordination gaps, and consistency risks
Security and compliance assessment
- PHI / PII Data Flow Tracing: Systematic identification of sensitive data exposure across event payloads, APIs, and storage
- HIPAA Compliance Gap Analysis: Service-by-service assessment against HIPAA requirements with prioritised remediation
- CI/CD Security Review: Pipeline configuration assessment covering secrets management, access controls, and deployment gates
- Secrets & Credential Audit: Identification of hardcoded credentials, unrotated tokens, and insecure secret handling patterns
Observability and performance review
- Monitoring Coverage Assessment: Evaluation of Datadog, PagerDuty, and team-owned monitoring standards across services
- Distributed Tracing Gap Analysis: Assessment of end-to-end trace capability across service boundaries
- Performance Bottleneck Identification: Database query analysis, queue depth patterns, and peak load handling review
- Testing Strategy Assessment: Test coverage, CI/CD automation quality gates, and production-environment parity evaluation
Technology
Technology stack
The languages, infrastructure and tooling we assess most often.
Languages and frameworks
- Ruby on Rails
- Python (Django, FastAPI)
- Node.js
- Java / Spring Boot
Infrastructure assessed
- AWS (Lambda, ECS, RDS)
- Microsoft Azure
- Kubernetes / VMware
- On-Premises SQL Server
Observability tools
- Datadog
- PagerDuty
- CloudWatch
- Sentry
CI/CD and source control
- GitHub Actions
- Jenkins
- GitLab CI/CD
- Terraform
Industries
Industry applications
Healthcare and health IT
- HIPAA and PHI compliance gap assessment
- Prior authorization and claims processing platform audits
- EHR integration and data integrity review
Financial services
- Payment processing reliability assessment
- API security and credential management review
- Regulatory compliance gap analysis
Technology and SaaS
- Pre-acquisition technical due diligence
- Pre-migration architecture assessment
- Production reliability review for scaling teams
Enterprise and consulting
- Platform consolidation readiness assessment
- Multi-team observability and monitoring standardisation
- Architecture documentation for undocumented legacy systems
Case study
Featured success story
Healthcare
Healthcare technology platform: enterprise microservices audit
88 Ruby/Rails microservices fully audited and dependency-mapped in 8 weeks. Active PHI exposure was identified in an event payload travelling to 4 subscribers, surfaced with file and line evidence. The ePAmotron retry gap and circuit breaker absence were documented as critical reliability risks, a complete PA lifecycle map was produced with 19/20 steps code-confirmed, and a prioritised remediation roadmap was delivered for reliability, security, and HIPAA compliance.
Read full case study- Microservices audited and mapped
- 88
- Time to complete the audit
- 8 weeks
- Subscribers receiving the exposed PHI payload
- 4
- PA lifecycle steps confirmed in code
- 19/20
Methodology
Our methodology
- 01
Scope definition and access setup
Engagement boundary agreed, codebase access provisioned, stakeholder workshops scheduled.
- 02
Architecture reconnaissance
Service inventory, dependency mapping, and domain structure documented from code, Confluence, and engineering interviews.
- 03
Deep code review
Systematic analysis of high-traffic and high-risk services across all 8 assessment areas with file and line-level evidence.
- 04
Observability and tooling analysis
Datadog, PagerDuty, and CI/CD pipeline assessment against engineering best practices.
- 05
Risk register construction
Findings prioritised by severity (RED / AMBER / GREEN) with evidence-backed rationale for each.
- 06
Remediation roadmap delivery
Quick wins and long-term initiatives structured by effort, impact, and dependency order.
- 07
Stakeholder presentation
Executive summary and engineering deep-dive presentations delivered to relevant audiences.
Why Agility
Why choose Agility.AI
Evidence-first methodology
Every finding is backed by specific code evidence at file and line level. We do not produce risk ratings based on assumptions, interviews alone, or pattern-matching without confirmation.
Production system experience
Our assessments are conducted by engineers who build and operate production systems at scale, not auditors working from checklists. We understand what the risks actually mean in live environments.
Independent perspective
As an external party with no stake in the existing architecture, we surface issues that internal teams normalise over time: the gaps that are genuinely invisible from inside.
Actionable, not academic
Our deliverables are remediation roadmaps, not audit reports. Every finding comes with a specific, prioritised recommendation that an engineering team can act on immediately.
FAQ
AI and application audits: common questions
Everything you need to know about our AI audit and assessment process.
What is included in a standard audit engagement?
A standard engagement covers service architecture mapping, reliability and error handling review, observability assessment, security and compliance gaps, CI/CD pipeline review, and a prioritised remediation roadmap. We scope coverage based on service count and timeline.
Do you write code fixes as part of the audit?
No. Our audit engagements are assessment-only. We identify and document issues with specific evidence and recommendations. Remediation execution is a separate engagement if required.
How do you handle confidential codebases?
Under NDA with strictly scoped access. We work within client-provisioned VPN or VDI environments, access only the repositories within the agreed scope, and produce no external copies of code or findings.
What size engineering organisations do you assess?
We have assessed platforms with 88 microservices across 13 engineering teams. We scale scope and team size to match the engagement, smaller platforms can be assessed in as little as 2 to 4 weeks.
Can an audit be used for pre-acquisition due diligence?
Yes. Our audit methodology and deliverables are structured to support technical due diligence for acquisition, investment, or merger scenarios, providing an independent, evidence-based view of platform risk and quality.
Ready for an independent assessment?
Schedule an assessment with our audit specialists to get an evidence-based view of your platform's reliability, security, and compliance posture.