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AI Platform Security

SOC 2 was built for traditional SaaS. Your AI platform introduces risks it was never designed to address. InfoGuard maps enterprise-grade security controls across your full AI stack — and delivers attestation that withstands the scrutiny of your most demanding enterprise buyers.

Why AI Changes the SOC 2 Risk Model

Standard SOC 2 controls address access management, availability, and data confidentiality. They do not account for the material risks introduced by large language models, retrieval-augmented generation pipelines, autonomous agents, and model orchestration layers. As AI becomes embedded in the products your customers rely on, the security controls governing those systems must be visible, documented, and auditable — or enterprise procurement teams will flag your SOC 2 report as insufficient.

AI-Specific Risks InfoGuard Addresses:

PROMPT INJECTION : Unauthorized instructions embedded in user input that manipulate model behavior, bypass security controls, or expose sensitive data without authorization.

MODEL MANIPULATION : Adversarial inputs that cause unreliable, biased, or dangerous model outputs, leading to bad business decisions and reputational harm.

RAG ABUSE : Exploitation of Retrieval-Augmented Generation systems to leak sensitive enterprise documents, expose PII, or produce misleading responses that erode customer trust.

DATA POISONING : Malicious training or retrieval data that corrupts model behavior, causing customer harm, regulatory exposure, and trust erosion.

UNCONTROLLED OUTPUT : AI systems producing unfiltered, hallucinated, or policy-violating content that create regulatory liability and reputational damage.

SUPPLY CHAIN RISK : LLM providers, embedding APIs, and orchestration frameworks that introduce third-party risks a standard vendor management program was never built to evaluate.

InfoGuard’s AI-Aligned SOC 2 Framework

InfoGuard applies SOC 2 controls across every layer of your AI stack — from user interface to enterprise data sources — with documentation that reflects how your AI system actually operates.

IDENTITY & ACCESS GOVERNANCE :  Authentication, authorization, and access management controls across users, APIs, and agents.

DATA ENCRYPTION: Encryption of data in transit and at rest — including vector embeddings, model inputs,
and enterprise data sources used in RAG pipelines.

INPUT & OUTPUT CONTROLS : Input validation, sanitization, prompt integrity controls, and output filtering to prevent
injection attacks and policy violations.

SECURE APIs & ORCHESTRATION :  Secure API design, MCP tool governance, orchestration security with audit logging and rate limiting.

LOGGING, MONITORING & CHANGE CONTROL :  Continuous monitoring of AI system behavior, anomaly detection, and rigorous change management
across model and pipeline updates.

DATA CLASSIFICATION & PRIVACY : Data classification, retention policies, and privacy protection controls applied across the full AI data lifecycle.

Result: A SOC 2 report that stands up to AI risk scrutiny — and positions your product as a responsible, enterprise-grade AI platform.

ISO/IEC 42001 Integration

InfoGuard’s AI security practice is designed to integrate with ISO/IEC 42001, the international standard for AI Management Systems. Clients who achieve an InfoGuard AI-aligned SOC 2 attestation are positioned to pursue ISO 42001 certification without rebuilding their control framework from scratch. This dual-path approach is increasingly requested by enterprise buyers and regulators evaluating AI governance maturity.

Is Your AI Platform Ready for Enterprise Security Scrutiny?

Schedule a complimentary AI security readiness discussion with Dr. Roohparvar — a focused conversation about your AI stack, your customer security requirements, and the specific controls gaps that put your enterprise deals at risk.
Schedule AI Security Assessment