Secure AI Development: Build AI Systems That Are Secure by Design
Secure AI development integrates security practices throughout the AI development lifecycle, from data collection and model training through deployment and monitoring. Petronella Technology Group builds custom AI solutions with security embedded at every stage, ensuring your AI systems resist adversarial attacks, protect sensitive data, and meet regulatory requirements. Combining 24+ years of cybersecurity expertise with production AI engineering, we deliver AI that works and AI that is safe.
CMMC RP-1372. 24+ years in cybersecurity and AI. Free consultation.
Key Takeaways
- Only 23% of AI projects include security testing (Gartner 2024). Most AI systems are deployed with vulnerabilities that standard application security testing does not catch.
- Secure-by-design AI costs 60% less to fix than retrofitting security after deployment (NIST AI RMF). Building security in from the start avoids expensive rework.
- Petronella follows NIST AI Risk Management Framework and OWASP LLM Top 10 throughout development, ensuring your AI meets the highest security and safety standards.
- Every AI system we build includes prompt injection defenses, input validation, output filtering, and audit logging as standard features, not afterthoughts.
What We Deliver
Secure Architecture Design
AI system architecture with defense-in-depth: input sanitization, output filtering, model isolation, least-privilege API access, and encrypted data pipelines. Security is designed in, not bolted on.
Secure Data Pipeline Development
Training data collection, cleaning, and storage with access controls, provenance tracking, and bias detection. We prevent data poisoning attacks and ensure compliance with data handling regulations.
Prompt Engineering with Security
System prompts hardened against injection, jailbreaking, and extraction. Multi-layer prompt defenses, output validation, and content filtering ensure the model behaves as intended.
Secure API and Integration Layer
API gateway design with authentication, rate limiting, input validation, and abuse detection. Integrations with existing systems are audited for data leakage and privilege escalation paths.
Security Testing and Red Teaming
Adversarial testing against OWASP LLM Top 10 risks: prompt injection, insecure output handling, training data poisoning, model denial of service, and supply chain vulnerabilities.
Deployment and Monitoring
Secure deployment with container hardening, network isolation, and continuous monitoring for model drift, adversarial inputs, and performance degradation. Audit logs capture every interaction.
AI Development Approaches Compared
| Aspect | Standard AI Dev | Petronella Secure AI Dev |
|---|---|---|
| Security testing | None or basic | OWASP LLM Top 10 + red teaming |
| Prompt injection defense | Not addressed | Multi-layer defenses |
| Data pipeline security | Basic access controls | Provenance, encryption, bias detection |
| Compliance alignment | Not considered | NIST AI RMF, HIPAA, CMMC |
| Audit logging | Minimal | Full interaction audit trail |
| Ongoing monitoring | Performance only | Security + performance + drift |
Led by Craig Petronella
Craig Petronella founded Petronella Technology Group in 2002 with 30+ years of cybersecurity and AI expertise. A CMMC Registered Practitioner (RP-1372), Craig combines security-first thinking with deep AI engineering to deliver solutions that are both powerful and secure.
Frequently Asked Questions
Do you build custom AI applications or just secure existing ones?
What AI frameworks and platforms do you work with?
How do you prevent prompt injection?
Can you build AI for regulated industries?
What is NIST AI RMF?
Our Secure AI Development Stack
We build on production-proven tools and frameworks, selecting the right components for each project's security and performance requirements.
Inference Engines
vLLM for high-throughput production serving, llama.cpp for edge and resource-constrained deployments, TGI for Hugging Face model compatibility. All deployed within your security boundary with TLS encryption, API authentication, and network isolation. No inference data leaves your infrastructure.
Orchestration Frameworks
LangChain and LlamaIndex for RAG pipelines with input sanitization at every stage. Custom middleware for prompt injection detection, output validation, and PII filtering. Vercel AI SDK for TypeScript-based applications requiring real-time streaming with security controls.
Model Security Testing
Automated red teaming using Garak and custom adversarial prompt libraries. OWASP LLM Top 10 vulnerability scanning. Behavioral boundary testing to verify system prompt integrity under attack. Comprehensive penetration testing of API endpoints, authentication flows, and data egress paths.
Monitoring and Observability
Real-time monitoring for model drift, latency degradation, anomalous input patterns, and output toxicity. Every prompt and response logged to immutable audit storage. Alerting for jailbreak attempts, data exfiltration patterns, and resource abuse. Integration with your existing SIEM for unified security visibility.
Secure AI Development in Practice
Healthcare Document Processing: HIPAA-Compliant AI
A mid-size healthcare organization needed to automate processing of patient intake forms, insurance documents, and clinical notes. We built a secure document processing pipeline using a fine-tuned model running on private infrastructure. The system extracts structured data from unstructured documents with 97% accuracy while maintaining full HIPAA compliance. All PHI remains on-premise, audit logs track every document interaction, and role-based access controls ensure only authorized staff can access patient data through the AI system.
Defense Contractor Knowledge Base: CUI-Protected RAG System
A CMMC-bound defense contractor needed an internal knowledge base that could answer technical questions from engineering staff without exposing Controlled Unclassified Information to external AI providers. We deployed a RAG system using a self-hosted LLM with FIPS 140-2 compliant encryption, network segmentation isolating the AI from internet-facing systems, and granular access controls mapped to CUI categories. The system reduced engineering lookup time by 65% while maintaining full NIST 800-171 compliance.
Written and reviewed by
Craig Petronella
Founder and CTO of Petronella Technology Group, Inc. 30+ years in cybersecurity and AI engineering. CMMC Registered Practitioner (RP-1372), certified ethical hacker, and author. Building secure AI systems for regulated industries since 2002.
Related Services
Build Secure AI from Day One
Schedule a free consultation to discuss your AI project. We will assess requirements, recommend architecture, and deliver AI that is both powerful and secure.
Petronella Technology Group, Inc.
5540 Centerview Dr. Suite 200, Raleigh, NC 27606
Phone: 919-348-4912