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Security & Compliance

AI Security Framework

Secure AI systems end-to-end. AI introduces new attack vectors that traditional security doesn't cover. A comprehensive framework protects against prompt injection, model theft, and adversarial attacks.

What Is an AI Security Framework?

An AI security framework is a comprehensive set of policies, controls, and practices designed to protect AI systems from emerging threats. It addresses vulnerabilities unique to machine learning models, including prompt injection, data poisoning, model theft, adversarial attacks, and unintended data leakage.

The framework covers the entire AI lifecycle from data collection and model training through deployment and monitoring, integrating with existing cybersecurity infrastructure while addressing AI-specific risks.

Why It Matters

Protect Against AI-Specific Threats

Defend against prompt injection, jailbreaking, model extraction, and other attacks that target AI systems specifically.

Prevent Model Theft

Protect proprietary models and training data from extraction and unauthorized replication.

Ensure System Integrity

Detect and prevent data poisoning and adversarial inputs that could corrupt model behavior.

Maintain Trust

Build confidence with customers and stakeholders by demonstrating robust AI security practices.

Key Components

Input Validation

Filter and sanitize prompts to prevent injection attacks and malicious inputs.

Model Access Controls

Restrict model access, rate limiting, and authentication to prevent unauthorized use.

Adversarial Defense

Detect and mitigate adversarial inputs designed to manipulate model outputs.

Model Monitoring

Continuous monitoring for unusual behavior, data drift, and potential attacks.

Secure Model Storage

Encrypt and protect model weights, training data, and configuration files.

Output Filtering

Scan model outputs to prevent leakage of sensitive data or harmful content.

Maturity Levels

Not Started / Planning

No AI-specific security controls. Relying solely on traditional cybersecurity measures. No awareness of prompt injection or model theft risks.

In Progress / Partial

Basic input filtering and rate limiting. Some model access controls. Limited monitoring for adversarial attacks.

Mature / Complete

Comprehensive AI security framework with input validation, adversarial defense, model monitoring, secure storage, and output filtering. Regular security assessments and red team exercises for AI systems. Integration with enterprise security operations.

How to Get Started

  1. 1.
    Conduct AI Threat Modeling: Identify attack vectors specific to your AI systems and prioritize risks.
  2. 2.
    Implement Input Filtering: Deploy prompt validation and sanitization to block injection attacks.
  3. 3.
    Secure Model Assets: Encrypt model weights and restrict access to training data and model files.
  4. 4.
    Deploy Monitoring: Set up real-time monitoring for unusual patterns, performance degradation, and potential attacks.
  5. 5.
    Test with Red Team: Conduct adversarial testing to identify vulnerabilities before attackers do.

Ready to Secure Your AI Systems?

Get expert help building a comprehensive AI security framework that protects against emerging threats and vulnerabilities.