Enterprise AI Security: Protecting Your Organization's AI Future

🏗️ AI Infrastructure Security

• Model Protection: Securing proprietary AI models from theft and reverse engineering
• Training Data Security: Protecting sensitive training datasets and preventing data poisoning
• Compute Security: Securing GPU clusters and AI-specific hardware
• API Gateway Protection: Implementing robust authentication and authorization for AI services

🔐 Identity & Access Management

• Role-Based Access Control (RBAC): Fine-grained permissions for AI resources
• Attribute-Based Access Control (ABAC): Context-aware access decisions
• Zero Trust Architecture: Never trust, always verify for AI workloads
• Privileged Access Management: Securing high-privilege AI operations

📊 Data Governance & Privacy

• Data Classification: Automated identification of sensitive data in AI pipelines
• Privacy-Preserving AI: Implementing differential privacy and federated learning
• Data Lineage Tracking: Complete visibility into data flow through AI systems
• Consent Management: Ensuring proper consent for AI data processing

⚠️ Model Extraction & IP Theft

Impact: Loss of competitive advantage, financial damage

Mitigation: Model obfuscation, API rate limiting, behavioral analysis

⚠️ Data Poisoning Attacks

Impact: Model performance degradation, biased outcomes

Mitigation: Data validation, anomaly detection, secure training pipelines

⚠️ Adversarial Examples

Impact: Model misclassification, system bypass

Mitigation: Adversarial training, input preprocessing, ensemble methods

⚠️ Supply Chain Attacks

Impact: Compromised AI components, backdoor access

Mitigation: Dependency scanning, secure repositories, integrity verification

📋 Key Regulations

• EU AI Act: Risk-based approach to AI regulation
• GDPR: Privacy requirements for AI processing
• SOX: Internal controls for AI in financial reporting
• HIPAA: Healthcare AI privacy and security
• PCI DSS: Payment processing AI security

🚀 RESK AI Security Platform

• Centralized security management for all AI workloads
• Real-time threat detection and response
• Comprehensive compliance reporting
• Integration with existing security tools (SIEM, SOAR)

🔧 Professional Services

• AI security architecture design
• Security assessment and penetration testing
• Compliance consulting and auditing
• 24/7 managed security services

What is enterprise AI security?

Enterprise AI security is the set of policies, controls and tooling that protects AI systems at organization scale: LLM firewalls and input/output filtering, role-based access controls for models and agents, AI-specific pentesting, data protection, and governance frameworks that keep deployments compliant.

How is enterprise LLM security different from classic application security?

LLMs add attack surfaces that traditional appsec does not cover: prompt injection and prompt leaking, jailbreaks, insecure tool and agent permissions, training-data poisoning, and uncontrolled model outputs. Enterprise LLM security extends appsec with generation-time enforcement, capability bitmasks per role, and continuous monitoring of model behavior.

Where should an enterprise start with AI security?

Start with an inventory of AI usage and an AI-specific risk assessment, then enforce a control point in front of every model call — an LLM firewall with role-based access control — and monitor it. RESK provides the open-source stack (ReskSafety, resk-llm, resk-logits) plus pentesting and governance services to operationalize this.