Building Secure AI Applications

LLMs and GenAI are paving the way for almost all modern applications and transforming all organizations by providing automation, natural language processing, and tailored user experiences. However, these rapid technological advancements come with a unique set of security, privacy, and ethical challenges that require all parties involved to take appropriate measures.

This book will offer a comprehensive guide to security of AI-integrated applications, providing readers with knowledge of the principles of AI application security with practical and actionable insights, and enabling them to identify threats, understand risks, utilize different testing methods, and integrate security best practices at each stage throughout the software development lifecycle.

By the end of this book, the readers will have a holistic perspective of all the critical areas of AI-based applications security. This deep understanding will help readers make secure choices for AI developments/integrations, assess vulnerabilities, develop mitigation techniques, and incorporate privacy and ethical AI practices. The readers can create safe, compliant, and responsible AI applications.

WHAT YOU WILL LEARN
● Foundational understanding of securing LLM and GenAI developments.
● Data security and privacy, compliance, and ethical considerations.
● Advanced security controls, monitoring, incident management, and evaluation techniques.
● Security of deployment environments and integration with other organizations’ infrastructure.
● Applying Zero Trust to LLM architectures.
● Automating incident response and anomaly detection.

WHO THIS BOOK IS FOR
This book is intended for security professionals, scholars, software developers/architects/managers, IT leaders, or anyone interested in technological advancements. It will help folks involved in developing and safeguarding GenAI/LLM infrastructures and integrating AI/GenAI/LLMs into their applications. Readers are expected to understand application development, GenAI/LLMs use cases, and fundamental security concepts.

1. Foundations of Application Security and AI Systems
2. Building Secure Framework
3. Authentication, Authorization, and API Security
4. Defending I/O Validation and Filtering
5. Protecting Data Integrity and Privacy
6. Verifying Model and Data Integrity
7. Ensuring Fairness, Transparency, and Accountability
8. Evaluating Testing, Penetration, and Red Teaming
9. Continuous Monitoring and Incident Response
10. Securing AI Integration
11. Scaling AI Security
12. Navigating Compliance and Regulatory
13. Future of Secure AI

● Parth Shah, CISSP, is a senior security research project manager at Microsoft and a leading expert in application security for AI and large language model (LLM), integrated systems. With over 15 years of experience in cybersecurity, he specializes in application security, cloud security, AI security, incident response, and secure-by-design architectures for large-scale production environments. Parth holds a master’s degree in cybersecurity and leadership from the University of Washington and a bachelor’s degree in computer engineering from G. H. Patel College of Engineering and Technology, India. He serves as president of the Information Systems Security Association (ISSA) Rainier Chapter (Tacoma, WA), is an AI Frontier Network (AIFN) Ambassador contributing to global AI security initiatives, and is an honorary member of the Center for Cyber Security Studies and Research (CFCS2R). He is a Hall of Fame inductee in international bug bounty programs and has published extensively in IEEE conferences, ISSA publications, and AI journals. His work focuses on LLM API security, secure GenAI architectures, prompt- injection defenses, model abuse prevention, and AI supply-chain risk mitigation.

● Harshil Shah is an application security engineer at American Family Insurance, where he leads risk-based vulnerability orchestration and strategic security triaging. With an extensive background spanning big data, machine learning, and cybersecurity, Harshil specializes in the critical intersection where data-intensive systems meet modern security frameworks. His work focuses on bridging the gap between engineering scale and defensive integrity, particularly in the context of proactive, risk-oriented remediation. Throughout his career, Harshil has built secure data environments and has led automated security initiatives for organizations across India and the USA. Harshil earned his master’s in cybersecurity and leadership from the University of Washington and holds a bachelor’s in computer engineering. This book is the culmination of his career-long curiosity in integrating data science with cybersecurity to build resilient, intelligent systems for the future.