Enterprise Security
Production-grade security from day one. Zero-trust architecture, AES-256 encryption, comprehensive audit logging, and compliance-ready infrastructure aligned with NIST AI RMF, SOC 2, HIPAA, and GDPR.
Security Features
Zero Trust Architecture
Never trust, always verify. Every request is authenticated and authorized regardless of origin. Dynamic authorization with short-lived tokens and workload identity prevents lateral movement.
End-to-End Encryption
AES-256 encryption at rest with FIPS 140-2/3 validated modules. TLS 1.3 for all data in transit. AI-specific encryption for training datasets, model weights, and inference payloads.
Comprehensive Audit Logging
Every action recorded with timestamps, user IDs, and context. Integrated with SIEM/SOAR for behavioral baselines, threat detection, and automated response workflows.
Role-Based Access Control
Granular RBAC and ABAC permissions ensure users only access what they need. Least privilege principles prevent privilege escalation across your AI infrastructure.
Compliance Ready
We architect systems that meet or exceed industry compliance standards from the ground up—integrated into GRC frameworks with cross-functional teams for agile governance.
SOC 2 Type II
Operational security controls with audit trails, access controls, and quarterly risk reviews
GDPR
Data minimization, pseudonymization, DPIA for high-risk AI, and consent management
HIPAA
PHI encryption (AES-256+), access logging, breach detection, and BAA-ready architecture
ISO 27001
Information security management aligned with NIST AI RMF and ISO 42001 standards
CCPA
California consumer privacy with automated data mapping and rights management
PCI DSS
Payment card security for AI systems processing financial data
How We Secure AI Systems
Data Isolation
Your data never mingles with other tenants. Dedicated encryption keys, isolated storage, and separate processing environments. We classify all AI assets and data by sensitivity to enforce protections.
Prompt Security
Input sanitization, prompt injection detection, and output filtering prevent AI-specific attack vectors. Real-time data protection detects sensitive data in prompts with automatic masking and redaction.
Model Governance
Control which models access your data, set usage policies, and maintain complete oversight of AI behavior. We vet third-party models and libraries, scanning containers for vulnerabilities.
Incident Response
24/7 monitoring with EDR, CSPM, and automated threat detection. Rapid response protocols, penetration testing, and transparent communication when issues arise.
Security Architecture
├── Authentication (OAuth 2.0 / SAML / SSO)
├── Authorization (RBAC / ABAC / Least Privilege)
├── Session Management (Secure cookies, JWT rotation)
└── Input Validation & Sanitization
├── Prompt Injection Detection & Prevention
├── Output Filtering & PII Redaction
├── Model Access Controls & Usage Policies
├── Third-Party Model/Library Vetting
└── Usage Monitoring & Rate Limiting
├── Encryption at Rest (AES-256, FIPS 140-2/3)
├── Encryption in Transit (TLS 1.3 minimum)
├── Data Isolation & Tenant Separation
├── AI-Specific: Model weights & inference encryption
└── Backup & Disaster Recovery
├── Network Segmentation & Firewalls
├── DDoS Protection & WAF
├── EDR, CSPM, SIEM/SOAR Integration
├── Container Scanning & Pod Security
└── 24/7 Security Monitoring & Alerting
Implementation Roadmap
Assess & Inventory (Week 1-2)
Catalog all AI agents, models, datasets, and shadow deployments. Classify data by sensitivity using our identify-classify-evaluate-prioritize framework.
Deploy Foundational Controls (Week 3-4)
Implement zero-trust authentication, RBAC, encryption, and monitoring. Integrate with existing SIEM/SOAR for unified visibility.
Refine & Test (Week 5-6)
Conduct penetration testing, red team exercises, and team training on AI-specific threats. Document everything for compliance audits.
Scale & Maintain (Ongoing)
Establish quarterly risk reviews, continuous monitoring, and agile governance. Scale to full AI enablement hub for compliant enterprise access.
Frequently Asked Questions
What is zero-trust architecture for AI systems?
Zero-trust architecture means never trusting and always verifying—every request is authenticated and authorized regardless of origin, using dynamic authorization with short-lived tokens and workload identity to prevent lateral movement.
How do you secure AI systems against prompt injection?
We secure AI systems against prompt injection with input sanitization, prompt injection detection, and output filtering, plus real-time data protection that masks and redacts sensitive data in prompts automatically.
Which compliance standards do you support?
We architect systems to meet SOC 2 Type II, GDPR, HIPAA, ISO 27001, CCPA, and PCI DSS, aligned with the NIST AI RMF and ISO 42001 standards and integrated into your GRC frameworks.
How is our data kept isolated from other clients?
Your data is kept isolated through dedicated encryption keys, isolated storage, and separate processing environments, so it never mingles with other tenants, with all AI assets classified by sensitivity to enforce protections.
How long does enterprise AI security implementation take?
Our implementation roadmap runs about six weeks: assess and inventory (weeks 1-2), deploy foundational controls (weeks 3-4), and refine and test with penetration testing (weeks 5-6), followed by ongoing scaling and quarterly risk reviews.