AI Security Framework

Framework Overview

A production-ready methodology for assessing AI system security across the entire lifecycle

Data Security

Protect training data, inference inputs, and model outputs from poisoning, manipulation, and unauthorized access.

Model Security

Secure model architecture, prevent extraction, and defend against adversarial attacks and model inversion.

Deployment Security

Ensure secure deployment practices, API protection, and runtime monitoring for AI systems.

Supply Chain Security

Audit AI dependencies, model repositories, and third-party components for vulnerabilities.

AI Governance

Establish policies, compliance frameworks, and ethical guidelines for AI system security, including Shadow AI detection and management.

Incident Response

Develop response procedures for AI security incidents, model failures, and data breaches.

Security Controls

Industry-standard security controls mapped to MITRE ATLAS and OWASP LLM Top 10

Preventive Controls

Input validation, access controls, secure coding practices

Detective Controls

Monitoring, logging, anomaly detection, behavioral analysis

Corrective Controls

Incident response, model retraining, security patches

OWASP LLM01

Prompt Injection Prevention

OWASP LLM06

Sensitive Information Disclosure

MITRE ATLAS

Adversarial Threat Landscape

Security Assessment Methodology

Take a guided journey through AI security assessment methodology

Click on each step to explore detailed procedures, tools, and best practices

Scope Definition

Define assessment boundaries and objectives

Start

Asset Discovery

Identify and catalog AI system components

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Threat Modeling

Map potential attack vectors and threats

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Vulnerability Assessment

Scan for security weaknesses and misconfigurations

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Penetration Testing

Simulate real-world attacks and exploits

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Reporting

Document findings and provide recommendations

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AI Security Risk Matrix

Advanced risk assessment tool for AI systems with comprehensive scoring capabilities

Risk Assessment Definitions

Likelihood Scale

Very High (5): Almost certain to occur (>90% probability)

High (4): Likely to occur (70-90% probability)

Medium (3): Possible to occur (30-70% probability)

Low (2): Unlikely to occur (10-30% probability)

Very Low (1): Rare occurrence (<10% probability)

Impact Scale

Very High (5): Catastrophic business/operational impact

High (4): Major business disruption or data loss

Medium (3): Moderate impact on operations

Low (2): Minor operational impact

Very Low (1): Negligible impact

Critical Risk (20-25)

Immediate action required. System shutdown may be necessary until mitigation is complete.

High Risk (15-19)

Urgent attention needed. Implement controls within 30 days.

Medium Risk (10-14)

Plan mitigation within 90 days. Monitor closely.

Low Risk (5-9)

Address through standard processes. Review annually.

Very Low Risk (1-4)

Acceptable risk level. Document and monitor periodically.

Impact →
Likelihood ↓

Very Low
(1)

Low
(2)

Medium
(3)

High
(4)

Very High
(5)

Very High (5)

High (4)

Medium (3)

Low (2)

Very Low (1)

AI Risk Calculator

Calculate AI security risk using CVSS-style methodology with likelihood, impact, and asset criticality factors.

Threat Type:

Select the primary threat vector targeting your AI system

Asset Criticality:

How critical is this AI system to your business operations?

Attack Likelihood:

Probability of successful attack within 12 months

Business Impact:

Total business impact including downtime, data loss, reputation

AI Security Risk Scenarios

Adversarial Attacks

Carefully crafted inputs designed to fool AI models

Critical Risk

Data Poisoning

Malicious data injection during training or inference

High Risk

Model Extraction

Unauthorized copying or reverse engineering of AI models

High Risk

Prompt Injection

Manipulating LLM behavior through crafted prompts

Medium Risk

Privacy Leakage

Unintended exposure of sensitive training data

High Risk

Bias Exploitation

Leveraging algorithmic bias for malicious purposes

Medium Risk

AI Security Architecture

Interactive visual representation of AI security framework architecture with component details

Component Analysis

Click on any architectural component to explore detailed security considerations and potential vulnerabilities. Each layer represents critical security checkpoints in the AI system architecture.

Click Components: View detailed security analysis for each component

Hover Layers: Highlight related architectural elements

Security Focus: Each component represents potential attack surfaces

User Interface Layer

External access points and client applications

Web Application

Mobile App

API Client

Application Layer

Business logic, input/output handling, and plugin management

Agent/Plugin Management

Input Handling

Output Handling

Model Layer

AI model storage, serving, training, and evaluation infrastructure

Model Storage Infrastructure

Model Serving Infrastructure

Training & Tuning

Model Frameworks & Code

Evaluation

Infrastructure Layer

Data storage, processing, and filtering systems

Data Storage Infrastructure

Training Data

Data Filtering & Processing

Data Sources

External data providers and input sources

External Sources

AI Security Methodology Guide

Framework Overview

Industry-leading AI security assessment methodology integrating MITRE ATLAS, OWASP LLM Top 10, and industry best practices.

Assessment Checklists

Detailed checklists for each assessment phase with MITRE ATLAS technique mapping and validation criteria.

Risk Assessment

Risk scoring algorithms and matrices aligned with ATLAS threat landscape and business impact analysis.

Document Sections:

Phase 1: Preparation and Scoping

Phase 2: Asset Identification

Phase 3: Threat Modeling

Phase 4: Vulnerability Assessment

Phase 5: Penetration Testing

Phase 6: Reporting & Remediation

Framework Integration:

MITRE ATLAS

OWASP LLM Top 10

NIST AI RMF

CSA AI Controls

Download Full Guide (PDF)

AI Security Assessment Playbook Example

Target System: Large Language Model API Service

Scope: Cloud-based LLM service with REST API endpoints, user authentication, and data processing capabilities

Timeline: 2-week comprehensive security assessment

Team: 3 security specialists (AI Security, Network Security, Application Security)

Phase 1: Reconnaissance (Days 1-2)

OSINT Collection: Public API documentation, GitHub repositories, tech stack identification
Attack Surface Mapping: Identify all endpoints, authentication mechanisms, data flows
Threat Modeling: Apply MITRE ATLAS framework to identify potential attack vectors

Tools: Nmap, Burp Suite, Custom OSINT scripts

Phase 2: Input Validation Testing (Days 3-5)

Prompt Injection: Test for direct and indirect prompt injection vulnerabilities
Input Sanitization: Evaluate filtering and validation mechanisms
Context Manipulation: Assess system prompt protection and context boundaries

Tools: Custom prompt injection payloads, Automated fuzzing tools

Phase 3: Data Security Assessment (Days 6-8)

Training Data Inference: Attempt to extract training data through model queries
PII Leakage: Test for personal information exposure in model responses
Data Storage Security: Evaluate encryption, access controls, and backup security

Tools: Model inversion scripts, PII detection tools, Database security scanners

Phase 4: Model Security Testing (Days 9-11)

Model Extraction: Attempt to reverse-engineer model architecture and parameters
Adversarial Attacks: Generate adversarial inputs to test model robustness
Bias and Fairness: Evaluate model outputs for discriminatory patterns

Tools: Adversarial ML libraries, Model extraction frameworks, Bias detection tools

Phase 5: Infrastructure Security (Days 12-13)

API Security: Authentication bypass, rate limiting, and authorization flaws
Container Security: Docker/Kubernetes misconfigurations and vulnerabilities
Network Security: TLS configuration, certificate validation, network segmentation

Tools: SSLyze, Docker security scanners, Kubernetes security tools

Phase 6: Reporting & Remediation (Day 14)

Vulnerability Classification: CVSS scoring, MITRE ATLAS TTPs mapping
Risk Assessment: Business impact analysis and remediation prioritization
Recommendations: Specific technical and procedural security improvements

Deliverables: Executive summary, Technical report, Remediation roadmap

Key Findings Example

Critical: Prompt Injection Vulnerability (CVSS 9.1)

Description: System prompt can be overridden through indirect injection via user-uploaded documents, allowing attackers to manipulate AI behavior and extract sensitive information.

Recommendation: Implement robust input sanitization, context isolation, and output filtering mechanisms.

High: Training Data Inference (CVSS 7.8)

Description: Model responses contain verbatim training data, potentially exposing proprietary information and personal data.

Recommendation: Apply differential privacy techniques and implement data anonymization in training pipelines.

Medium: Insufficient Rate Limiting (CVSS 5.3)

Description: API endpoints lack proper rate limiting, enabling resource exhaustion and potential DoS attacks.

Recommendation: Implement tiered rate limiting based on user authentication levels and request complexity.

This playbook follows industry standards including MITRE ATLAS, OWASP LLM Top 10, and NIST AI RMF guidelines.

AI Security Testing Tools

MITRE ATLAS Integration

Our testing tools implement MITRE ATLAS techniques and tactics for comprehensive AI security assessment with automated SAST/DAST capabilities.

Available Tools:

AI Model Security Scanner

Automated vulnerability assessment for ML models including adversarial robustness testing and model extraction detection.

Adversarial Testing Model Extraction Privacy Attacks

Data Pipeline Security Analyzer

Comprehensive analysis of data ingestion, processing, and storage security with poisoning attack detection.

Data Poisoning Input Validation Pipeline Security

LLM Security Tester

Specialized testing for Large Language Models including prompt injection, jailbreaking, and output manipulation detection.

Prompt Injection Jailbreaking Output Manipulation

API Security Scanner

Automated API security testing for AI service endpoints including authentication bypass and injection attacks.

Auth Testing Injection Tests Rate Limiting

MITRE ATLAS Technique Coverage:

Initial Access

ML Supply Chain Compromise

Execution

Serverless Execution

Defense Evasion

Adversarial Perturbations

Discovery

ML Artifact Enumeration

Visit GitHub Repository

Note: These tools are designed for authorized security testing only. Ensure proper authorization before testing any AI systems.

Interactive Architecture Diagram

Professional UML-Style Architecture

This interactive diagram presents a comprehensive 5-layer AI security architecture with enterprise-grade design and professional UML styling, similar to Google Cloud and AWS architecture diagrams.

Multi-Layer Architecture

Five distinct architectural layers: User Interface, Application, AI Model, Infrastructure, and Data Sources - each with color-coded components and clear boundaries.

Interactive Security Tour

SAIF-inspired interactive tour showcasing 5 critical AI security risks with introduction/exposure/mitigation indicators across all architectural layers.

Risk Analysis System

Visual risk indicators showing where threats are introduced, exposed, and mitigated throughout the system architecture with color-coded severity levels.

Architecture Layers Overview:

User Interface Layer

Web applications, mobile apps, and API clients - external access points and user interaction components.

Application Layer

Business logic, agent/plugin management, input/output handling, and application processing components.

AI Model Layer

Model storage, serving infrastructure, training/tuning systems, frameworks, and evaluation components.

Infrastructure Layer

Data storage infrastructure, training data management, and data filtering/processing systems.

Data Sources

External data providers, APIs, databases, and various input sources feeding the AI system.

Interactive Security Risk Tour:

5 Critical AI Security Risks

Data Poisoning

Malicious data injection attacks

Model Extraction

Unauthorized model copying

Adversarial Attacks

Input manipulation attacks

Prompt Injection

LLM behavior manipulation

Privacy Leakage

Sensitive data exposure

Interactive Features

Risk Tour Navigation

Click "Start Risk Tour" to begin guided exploration of security vulnerabilities across all architectural layers.

Layer Highlighting

Interactive layer highlighting shows risk introduction, exposure, and mitigation points with visual indicators.

Professional Design

Production-grade UML styling with clean typography, color-coded components, and modern gradient effects.

Responsive Layout

Fully responsive design that adapts to different screen sizes while maintaining professional appearance.

This professional UML-style architecture diagram provides comprehensive visualization of AI security considerations across all system layers.

AI Governance & Policy Framework

Organizational AI Governance

Establish organization-wide policies, compliance frameworks, and ethical guidelines for AI system security across the organization, including detection and management of unsanctioned AI usage.

Policy Framework

AI usage and development policies
Data governance and privacy protection
Ethical AI principles and guidelines
Risk management frameworks
Incident response procedures

Compliance & Oversight

GDPR and privacy regulation compliance
Industry-specific regulatory requirements
AI model validation and testing
Audit trails and documentation
Third-party vendor assessments

Shadow AI Management

Detection of unsanctioned AI tools
Risk assessment of unauthorized usage
Employee training and awareness
Approved AI catalog maintenance
Monitoring and enforcement controls

What is Shadow AI?

Shadow AI refers to the unsanctioned use of artificial intelligence tools or applications by employees or end users without the formal approval or oversight of the information technology (IT) department.

This unauthorized usage creates significant security, compliance, and governance risks that organizations must proactively identify and manage through strategic governance frameworks.

Implementation Strategy:

Discovery & Assessment

Network traffic analysis for AI services
Application inventory and risk assessment
User surveys and usage patterns

Policy & Controls

AI usage policies and procedures
Approved tool catalog maintenance
Training programs and awareness

Monitoring & Enforcement

Continuous compliance monitoring
Automated detection systems
Incident response and audits

Effective AI governance requires balanced policy enforcement, user education, and technological controls.

Framework Overview

Data Security

Model Security

Deployment Security

Supply Chain Security

AI Governance

Incident Response

Attack Surface Mapping

Data LayerAttacks

Model LayerAttacks

DeploymentAttacks

InfrastructureAttacks

Security Controls

Preventive Controls

Detective Controls

Corrective Controls

OWASP LLM01

OWASP LLM06

MITRE ATLAS

Security Assessment Methodology

AI Security Risk Matrix

Risk Assessment Definitions

Likelihood Scale

Impact Scale

Critical Risk (20-25)

High Risk (15-19)

Medium Risk (10-14)

Low Risk (5-9)

Very Low Risk (1-4)

AI Risk Calculator

Recommended Actions:

AI Security Risk Scenarios

Adversarial Attacks

Data Poisoning

Model Extraction

Prompt Injection

Privacy Leakage

Bias Exploitation

AI Security Architecture

Component Analysis

AI Security Assessment Checklist

Framework Resources

Methodology Guide

Testing Tools

AI Security Methodology Guide

Framework Overview

Assessment Checklists

Risk Assessment

Document Sections:

Framework Integration:

AI Security Assessment Playbook Example

Target System: Large Language Model API Service

Phase 1: Reconnaissance (Days 1-2)

Phase 2: Input Validation Testing (Days 3-5)

Phase 3: Data Security Assessment (Days 6-8)

Phase 4: Model Security Testing (Days 9-11)

Phase 5: Infrastructure Security (Days 12-13)

Phase 6: Reporting & Remediation (Day 14)

Key Findings Example

Critical: Prompt Injection Vulnerability (CVSS 9.1)

High: Training Data Inference (CVSS 7.8)

Medium: Insufficient Rate Limiting (CVSS 5.3)

AI Security Testing Tools

MITRE ATLAS Integration

Available Tools:

AI Model Security Scanner

Data Pipeline Security Analyzer

LLM Security Tester

API Security Scanner

MITRE ATLAS Technique Coverage:

Initial Access

Execution

Defense Evasion

Discovery

Interactive Architecture Diagram

Professional UML-Style Architecture

Multi-Layer Architecture

Interactive Security Tour

Risk Analysis System

Data Layer
Attacks

Model Layer
Attacks

Deployment
Attacks

Infrastructure
Attacks