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The client had built a sophisticated enterprise AI/ML platform powering intelligent document processing, customer analytics, automated auction pricing, and generative AI-driven Q&A workflows. The platform integrated large language models (LLMs), retrieval-augmented generation (RAG) pipelines, and custom ML inference engines across AWS and Azure — creating over 30 interconnected components with a vast attack surface.
Five critical security gaps demanded an independent, specialized assessment:
ioSENTRIX executed a structured, five-phase threat modeling engagement designed specifically for AI/ML infrastructure:
Phase 1: Information Gathering — Conducted in-depth interviews with 8 key stakeholders across AI security, architecture, software engineering, and cybersecurity disciplines. Reviewed all available documentation including architecture wikis, system design documents, and prior gap analyses to build a complete understanding of the platform's intended behavior and data flows.
Phase 2: System Decomposition — Mapped all 30+ platform components and defined trust zones for each. Built detailed control-flow diagrams capturing data flows across the GenAI service, Model Hub, API integration layers, data infrastructure, and cloud networking components spanning both AWS and Azure environments.
Phase 3: Threat Analysis & Modeling — Enumerated all underlying assets, existing security controls, and potential threat agents. Applied NIST SP 800-30 Rev. 1 for risk classification using a likelihood-impact severity matrix. Simultaneously applied the NVIDIA AI Red Team Assessment Framework to evaluate AI/ML-specific threat categories including model vulnerabilities, prompt injection vectors, RAG security, harm and abuse risks, and inference pipeline threats.
Phase 4: Attack Scenario Development — Built a comprehensive threat traceability matrix mapping each identified threat to specific assets, controls, and threat agents. Developed prioritized attack scenarios using STRIDE methodology for systematic identification of Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege threats across all platform components.
Phase 5: Remediation Planning — Developed specific, actionable remediation guidance for each of the 13 identified findings. Recommendations were prioritized by severity rating and aligned with the organization's risk appetite, business objectives, and regulatory obligations.
The assessment covered five core technology areas: the GenAI Platform (RAG pipelines, LLM orchestration, prompt services, guardrails, document vectorization), the Model Hub (ML inference pipelines, SageMaker batch transforms, model registry, MLOps workflows), API and Integration layers (API gateways, Lambda functions, authentication, PII masking), Data Infrastructure (vector databases, S3 document stores, DynamoDB audit logs, Snowflake data layers), and Cloud Security (AWS VPC, Azure ExpressRoute, Azure Content Safety, secrets management).
The threat modeling engagement delivered measurable, high-impact results across every dimension of the client's AI/ML security posture:
13 Threats Identified & Documented — A complete enumeration of security threats spanning AI/ML-specific attack vectors, access control gaps, data privacy exposures, infrastructure weaknesses, and compliance risks.
5 High-Severity Findings — Critical vulnerabilities discovered and documented with specific remediation guidance:
3 Moderate-Severity Findings — Including lack of proper isolation between staging and production model deployments, missing output validation in GenAI and Model Hub, and absence of user-level identity tracking in API calls.
4 Low-Severity Findings — Including inadequate model risk verification during staging, Lambda denial-of-service vectors, overly permissive S3 document store access, and cold-start latency affecting availability.
30+ Components Decomposed — Every component mapped with trust zones, data flows, and security controls documented.
100% Threat Traceability Matrix Coverage — Every identified threat mapped to specific assets, controls, and threat agents.
5-Week Engagement — Full phased assessment completed from kickoff to final deliverable.
2 Cloud Providers Assessed — Complete cross-cloud analysis of AWS and Azure with data flow mapping between providers.
Proactive Risk Reduction — Identified 5 high-severity threats before exploitation, including prompt injection vulnerabilities, missing RBAC enforcement in RAG pipelines, and unprotected ML inference pathways. The client gained a prioritized remediation roadmap enabling engineering teams to immediately address the most critical risks.
Regulatory Readiness — The threat model documentation directly supports the client's compliance obligations by providing evidence of proactive AI risk management aligned with NIST standards and emerging AI governance requirements. This is critical for financial institutions facing increasing regulatory scrutiny of AI deployments.
Cross-Cloud Security Visibility — Provided unprecedented visibility into security gaps at the intersection of AWS and Azure services, particularly around the Azure OpenAI integration and cross-cloud data transit paths. This multi-cloud perspective is essential as organizations increasingly distribute AI workloads across providers.
Future-Proof Security Posture — Established an AI/ML-specific security evaluation framework that can be applied to all future AI deployments, ensuring consistent and rigorous assessment as the platform evolves. New components can now be evaluated against the same criteria before entering production.
Data Privacy Protection — Uncovered sensitive data exposure risks in logging, storage, and document upload workflows. The client can now close data leakage pathways across their AI infrastructure, protecting customer PII and proprietary financial data from unauthorized access through AI components.
Accelerated Remediation — The severity-prioritized remediation roadmap with specific, actionable instructions for each finding reduced the window of exposure for high-severity threats. Engineering teams received clear implementation guidance rather than abstract security recommendations.
Threat Traceability for Ongoing Risk Management — The detailed mapping of threats to assets, controls, and threat agents enables the client to track remediation progress and maintain an up-to-date risk register for their AI infrastructure as the platform continues to evolve.
