AI Security Architecture Review
AI systems introduce attack surfaces that traditional security reviews do not cover. Model poisoning, prompt injection, training data exfiltration, adversarial inputs — these threats require a different kind of architecture review. We evaluate your AI infrastructure from data pipeline to model deployment to API exposure.
What Is an AI Security Architecture Review?
An AI security architecture review evaluates the security of your AI/ML infrastructure — from data collection and model training through deployment and inference. It identifies threats specific to AI systems that standard infrastructure reviews miss: model manipulation, data poisoning, prompt injection, and adversarial attacks.
The review covers the full AI lifecycle. Data pipelines, model registries, serving infrastructure, API endpoints, and the integration points between AI systems and your broader application architecture. Each component is assessed for both traditional security risks and AI-specific threat vectors.
What’s Covered
AI security across the lifecycle.
Model Deployment Security
Review of model serving infrastructure. Container security, model file integrity, access controls on model endpoints, and deployment pipeline security. How your models reach production and who can access them.
Data Pipeline Security
Security of training data flows. Data ingestion, preprocessing, feature stores, and training data access controls. Risks of data poisoning, tampering, and sensitive data leakage through model training.
AI API Security
Security of AI-facing APIs. Authentication, rate limiting, input validation, and abuse prevention for inference endpoints. Prompt injection defences and output filtering.
LLM Infrastructure Review
Security assessment of LLM deployment architecture. RAG pipelines, vector databases, embedding services, and the security boundaries between user input and model inference.
Access Control & Governance
Who can access training data, modify models, and deploy to production. Separation of duties for AI lifecycle. Model versioning, approval workflows, and rollback capabilities.
Monitoring & Observability
Detection capabilities for AI-specific attacks. Input anomaly detection, output monitoring, model performance drift, and abuse pattern identification. Visibility into how your AI systems are being used and attacked.
Our Approach
AI-specific threat analysis.
Architecture Mapping
We map your AI/ML infrastructure — data pipelines, training environments, model registries, serving infrastructure, and integration points. Understanding the full lifecycle is prerequisite to finding threats.
Threat Analysis
Each component analysed for AI-specific and traditional threats. Data poisoning vectors, model manipulation paths, API abuse scenarios, and data exfiltration through model outputs.
Control Evaluation
Existing security controls assessed against identified threats. Access controls, encryption, monitoring, and governance structures evaluated for coverage and effectiveness.
Remediation & Hardening
Prioritised security improvements with implementation guidance. Architecture changes, control additions, and monitoring enhancements. Practical recommendations that fit your AI development workflow.
Why Bravix AI Architecture Review?
Offensive AI Expertise
We conduct AI penetration testing — prompt injection, model extraction, adversarial attacks. Our architecture reviews are informed by hands-on exploitation of AI systems, not just theoretical threat modelling.
AI + Security Knowledge
Reviewing AI architecture requires understanding both ML systems and security engineering. Our consultants have both. We understand embedding spaces, attention mechanisms, and gradient attacks alongside traditional infrastructure security.
Full Lifecycle Coverage
From data collection to inference API, we review the entire chain. No component treated as out of scope because it is the data science team’s responsibility.
Practical Guidance
Recommendations calibrated to your AI maturity and development practices. We understand that AI development moves fast — our guidance balances security rigour with development velocity.
Review Your AI Security Architecture
AI-specific threats require AI-specific reviews. Secure your ML infrastructure from training to inference with assessments informed by offensive AI testing.