AI Security
Protect your data, models, and customer trust
Aligned with 2026 SOC 2 AI requirements, ISO 42001, and the NIST AI RMF.
Prompt Injection. RAG Security. Agents and MCP. Data Protection. AI Governance.
AI security basics
What is AI security?
AI security protects the models, data, pipelines, and agents behind your AI products, and the AI tools your people use every day. It covers large language models, retrieval-augmented generation (RAG), autonomous agents, MCP tool integrations, and third-party model providers.
Controls built for traditional SaaS, including standard SOC 2 controls, were not designed for these systems. Prompt injection, data leakage through retrieval, and unchecked agents create new security risk that enterprise buyers now ask about in every review.
Why it matters
Adopt AI without exposing your data or models
Faster enterprise deals
Pass the AI questions in every security review
Documented AI controls answer the questions enterprise procurement teams now ask, so deals do not stall in security review.
Documented AI controls answer the questions enterprise procurement teams now ask, so deals do not stall in security review.
No data leakage
Keep sensitive data out of prompts, outputs, and training
Classification, access control, and output filtering stop models and RAG pipelines from exposing PII or confidential documents.
Classification, access control, and output filtering stop models and RAG pipelines from exposing PII or confidential documents.
Controlled agents
AI agents do only what they are authorized to do
Granular authorization, MCP tool governance, and audit logging keep agents from taking unintended or harmful actions.
Granular authorization, MCP tool governance, and audit logging keep agents from taking unintended or harmful actions.
Protected reputation
Fewer harmful or wrong answers reach your customers
Input and output controls and monitoring catch manipulated, hallucinated, or policy-violating responses before they cause damage.
Input and output controls and monitoring catch manipulated, hallucinated, or policy-violating responses before they cause damage.
What we secure
Security across every layer of your AI stack
From the user interface to your enterprise data sources, we apply controls documented the way your AI system actually works.
Access
Identity and authorization
Authentication and granular authorization for users, APIs, and AI agents.
Data
Encryption and classification
Encryption and classification for model inputs, vector embeddings, and RAG data sources.
Inputs and outputs
Prompt and output controls
Input validation, prompt integrity controls, and output filtering against injection and policy violations.
Orchestration
APIs, agents, and MCP
Secure API design, MCP tool governance, audit logging, rate limiting, and change control.
AI-specific risks
The security risks standard controls miss
AI introduces security risks that firewalls, antivirus, and standard SOC 2 controls were never designed to address.
Area
What can go wrong
How InfoGuard helps
Prompt injection
Hidden instructions bypass controls or expose data
Input validation and prompt integrity controls
RAG abuse
Retrieval leaks confidential documents or PII
Access-aware retrieval and data classification
Data poisoning
Tampered data corrupts model behavior
Data source integrity and change control
Uncontrolled output
Hallucinated or policy-violating responses
Output filtering and behavior monitoring
Agents and tools
Agents take actions no one approved
Granular authorization and MCP tool governance
Accuracy and bias
Inaccurate, biased, or unexplainable decisions
Testing, human review, and drift monitoring
AI supply chain
Model and API providers add third-party risk
AI-specific vendor risk assessment
Why InfoGuard
AI security backed by audit and governance expertise
2026 SOC 2 for AI
Ready for the new 2026 SOC 2 AI requirements
We prepare your system description, AI risk assessment, AI governance, and vendor oversight to meet the new AICPA AI guidance, so your next SOC 2 report stands up to buyer scrutiny.
We prepare your system description, AI risk assessment, AI governance, and vendor oversight to meet the new AICPA AI guidance, so your next SOC 2 report stands up to buyer scrutiny.
ISO 42001 ready
A path to ISO 42001 without starting over
Controls are designed to carry into ISO 42001 and the NIST AI RMF, saving time, money, and resources later.
Controls are designed to carry into ISO 42001 and the NIST AI RMF, saving time, money, and resources later.
Zero Trust for AI
Every user, API, and agent verified
Granular authorization limits what each user, service, and agent can reach, so one compromise exposes far less.
Granular authorization limits what each user, service, and agent can reach, so one compromise exposes far less.
Seasoned leadership
Led by a CISO with 30+ years of experience
CISSP, CISA, CISM, and former FedRAMP 3PAO assessor experience applied to the newest technology risk.
CISSP, CISA, CISM, and former FedRAMP 3PAO assessor experience applied to the newest technology risk.
Our approach
From AI stack review to audit-ready controls
01
AI Stack Discovery
We map your models, data sources, RAG pipelines, agents, MCP tools, and third-party AI providers.
02
AI Risk Assessment
We assess each layer for prompt injection, data leakage, poisoning, output, agent, and supply chain risk.
03
Control Design
We design controls mapped to the 2026 SOC 2 AI requirements, ISO 42001, and the NIST AI RMF.
04
Implementation
We help your team put access, encryption, input and output, and orchestration controls in place.
05
Documentation & Evidence
We document controls the way your AI system actually works, ready for auditors and buyer reviews.
06
Monitoring & Change Control
We monitor AI behavior and keep controls current as models, prompts, and pipelines change.
FAQ
AI security questions, answered
AI security
Does SOC 2 cover AI risks?
The criteria are the same, but expectations have changed. In September 2026, the AICPA issued Q&A Section 9561: when AI is relevant to your service, auditors expect it to be addressed in your system description, risk assessment, AI governance, and vendor oversight. We prepare your AI controls and documentation to meet it. See our SOC 2 page for details.
What is prompt injection?
Prompt injection is when instructions hidden in user input or retrieved content manipulate an AI model into ignoring its rules, exposing data, or taking unintended actions.
Do you secure AI agents and MCP integrations?
Yes. We design granular authorization, tool governance, audit logging, and rate limiting so agents and MCP tools act only within approved limits.
How does this relate to ISO 42001 and the NIST AI RMF?
Our AI security controls are designed to carry directly into ISO 42001 certification and NIST AI RMF alignment. See our ISO 42001 and NIST AI RMF pages for details.
Scope, timing, and cost
We only use AI tools internally. Do you help with that?
Yes. We help set AI acceptable use policies, approved tools, and data controls so your people use AI without leaking sensitive data.
Which AI platforms and providers do you work with?
Any. Our controls apply whether you build on commercial model APIs, open-source models, or your own, in any cloud.
How long does an AI security engagement take?
An AI stack review and risk assessment typically takes a few weeks. Control implementation follows a prioritized plan.
How much does it cost?
Cost depends on the size and complexity of your AI stack. There is no flat fee. We provide a firm quote after a short scoping conversation.
AI security in practice
Your buyers are asking how your AI is secured
A clear, documented answer is now part of closing every enterprise deal.
Next step
Tell us where you stand
Tell us about your AI product or the AI tools your teams use. We’ll reply with next steps, whether that’s an AI risk assessment, AI-aligned SOC 2 controls, or ISO 42001 readiness.