AI Risk Management Platform for Enterprise

As enterprise adoption of generative AI accelerates, organizations face unprecedented challenges in balancing innovation with security. AI Risk Management is the systematic process of identifying, evaluating, and mitigating the security, compliance, operational, and financial risks associated with the use of AI tools and Large Language Models (LLMs).

Without a dedicated risk management framework, employees uploading confidential code, customer PII, or internal financials to tools like ChatGPT, Claude, and Gemini risk severe data breaches and regulatory non-compliance.

What is AI Risk Management?

AI Risk Management provides security and compliance teams with full visibility and control over how artificial intelligence applications process data within the enterprise. Unlike traditional web governance, AI risk management addresses the dynamic, interactive nature of prompt-based workflows and autonomous AI agents.

A modern AI risk management policy addresses three core pillars:

1. Visibility: Discovering every AI application, browser extension, and API endpoint accessed across enterprise endpoints (Shadow AI).
2. Control: Enforcing granular data loss prevention (DLP) rules directly on the client side to block sensitive data transfer *before* it leaves the local device.
3. Governance: Mapping AI usage to international compliance frameworks like NIST AI RMF, ISO 27001, SOC 2, HIPAA, and GDPR.

Key Risks from GenAI Adoption

Generative AI tools introduce unique vulnerability vectors that traditional network proxies and cloud firewalls are not built to detect:

• Proprietary Data Leakage: Employees pasting proprietary source code, intellectual property, product blueprints, or executive strategy into public AI chat interfaces.
• Regulatory Non-Compliance: Unintentional exposure of Personally Identifiable Information (PII), Protected Health Information (PHI), or financial records violating GDPR, HIPAA, or CCPA guidelines.
• Prompt Injection & Manipulation: Malicious inputs designed to bypass LLM guardrails or trick autonomous agents into leaking system prompts or executing unauthorized tasks.
• Unverified Third-Party AI Extensions: Browser plugins and developer extensions that capture background keystrokes or send internal context to unvetted third-party servers.

AI Risk Assessment Framework

Evaluating enterprise AI risk requires a structured, multi-tiered evaluation strategy:

1. Inventory & Discovery: Identify all active AI tools across endpoint devices, including official enterprise SaaS subscriptions and unsanctioned Shadow AI platforms.
2. Data Sensitivity Categorization: Classify prompt content in real time into sensitive categories—Source Code, API Keys, Passwords, Financials, PII, and Health Data.
3. Threat & Exposure Scoring: Calculate real-time risk scores for user actions based on the target AI model's data retention policies and training opt-out status.
4. Policy Enforcement: Dynamically apply actions—Block, Redact, Anonymize, or Alert—based on user role, department, and application context.

Continuous AI Monitoring

Static annual audits and periodic questionnaire surveys are insufficient for fast-paced AI usage. Continuous AI Monitoring continuously analyzes endpoint interactions as they occur:

• Local Clipboard Interception: Hooking OS-level copy-paste operations to inspect prompts in <10ms before text reaches the application layer.
• Browser & Desktop Agent Monitoring: Tracking prompt inputs across web applications, desktop LLM wrappers, and IDE extensions (Cursor, GitHub Copilot).
• Behavioral Anomaly Detection: Flagging unusual spikes in data volume, unexpected off-hours activity, or unauthorized bulk code transfers.

Compliance Mapping

TryAIDR simplifies compliance by automatically mapping all AI interaction logs and policy enforcements to established regulatory frameworks:

• NIST AI Risk Management Framework (AI RMF): Fulfills Govern, Map, Measure, and Manage functions through automated client-side telemetry.
• SOC 2 Type II: Satisfies Trust Services Criteria for Confidentiality (CC6.1) and Processing Integrity by verifying zero unauthorized data egress.
• ISO 27001:2022: Maps directly to Annex A controls for data leakage prevention and information transfer policies.
• GDPR & HIPAA: Enforces strict local redaction of personal identifiers and health records prior to network transmission.

How TryAIDR Reduces AI Risk

TryAIDR delivers an endpoint-native, client-first AI risk management platform that operates without slowing down employee productivity:

• Client-Side Processing: Prompts are inspected offline on the local endpoint, ensuring sensitive data is filtered before it ever touches a external network gateway.
• Instant Redaction & Blocking: Redacts sensitive items (API keys, secrets, PII) in under 10 milliseconds.
• Cryptographic Audit Trail: Generates tamper-resistant, cryptographically signed audit logs for seamless compliance reporting.
• Zero Cloud Latency: Keeps developer workflows smooth without introducing sluggish gateway round-trips.