Secure Coding in the Age of AI
Protecting Applications, Data, and Development Workflows in AI-Assisted Software Engineering
What's Included:
Key Highlights
- AI-specific threat modeling and risk categories
- Secure Development Lifecycle (SDL) adapted for AI workflows
- Prompt injection and model misuse prevention
- Authentication, authorization, and API hardening
- Dependency and supply chain security
- Secrets management and configuration isolation
- Cloud infrastructure hardening practices
- Security-focused AI-assisted code reviews
- Compliance considerations in AI environments
Overview
Master secure coding for AI-assisted development. Protect applications, APIs, prompts, infrastructure, and supply chains with secure-by-design engineering practices.
The Problem
AI-assisted development dramatically accelerates code generation—but speed introduces risk. Insecure defaults, unvalidated input, exposed secrets, and poorly reviewed dependencies can enter production before anyone notices.
Traditional security processes often lag behind AI-driven workflows, creating blind spots where vulnerabilities thrive. Developers may unknowingly introduce:
- Prompt injection vulnerabilities
- Authentication and authorization flaws
- Insecure API endpoints
- Leaked credentials in generated configs
- Supply chain exposure through automated dependencies
Without structured secure-by-design practices, AI acceleration becomes a liability.
The Solution
Secure Coding in the Age of AI provides a practical framework for integrating security directly into AI-assisted engineering workflows.
You will learn how to:
- Embed security into prompts and AI interactions
- Validate AI-generated code before integration
- Harden authentication, authorization, and API layers
- Secure dependencies and prevent supply chain compromise
- Manage secrets safely across environments
- Apply logging, monitoring, and detection strategies effectively
- Balance performance, usability, and security trade-offs
The result: faster development without sacrificing resilience, compliance, or trust.
About This Book
Secure Coding in the Age of AI is a practical, modern guide to building secure software in AI-assisted development environments. As coding assistants and large language models accelerate development speed, they also introduce new attack surfaces, hidden vulnerabilities, and security blind spots.
This book equips developers, architects, and security engineers with a structured framework for secure-by-default engineering in an AI-driven world. You will learn how to embed security into prompts, generated code, infrastructure, APIs, dependencies, and cloud workflows—before vulnerabilities reach production.
Built for Modern AI-Driven Engineering
This is not a generic cybersecurity book. It is a developer-first security guide focused on real engineering workflows:
- Securing AI-generated code before it ships
- Preventing prompt injection and data leakage
- Hardening authentication and authorization flows
- Protecting APIs in AI-driven systems
- Managing secrets safely in automated workflows
- Defending against supply chain and dependency risks
From Secure Coding to Secure Architecture
You’ll build skills progressively—from core secure coding principles to infrastructure-level defense:
- Secure Development Lifecycle (SDL) adapted for AI workflows
- Input validation and data protection strategies
- Dependency and supply chain security
- Cloud infrastructure hardening
- Secure logging and monitoring practices
- Compliance and regulatory awareness in AI systems
AI-Specific Threat Modeling
AI introduces new categories of risk. This book addresses them directly:
- Prompt injection attacks
- Model misuse and data exfiltration
- Insecure automation pipelines
- Unsafe default configurations generated by AI tools
This is not fear-driven security. It is disciplined engineering that allows you to move fast without sacrificing safety.
Who Is This Book For?
- Developers using AI coding assistants in production environments
- Security engineers adapting to AI-driven workflows
- Architects designing secure AI-integrated systems
- Tech leads responsible for secure-by-default codebases
- DevOps and cloud engineers securing automation pipelines
Who Is This Book NOT For?
- Readers looking for purely theoretical cybersecurity research
- People seeking only high-level “AI tips” without engineering depth
- Developers unwilling to adopt structured security practices
- Advanced penetration testers looking for exploit development manuals
Table of Contents
- The Security Landscape Has Changed
- Understanding AI-Specific Security Risks
- Secure Development Lifecycle (SDL) in an AI Workflow
- Input Validation & Data Protection
- Authentication & Authorization Hardening
- API Security in AI-Driven Applications
- Dependency & Supply Chain Security
- Secrets Management & Environment Security
- Secure Prompt Engineering
- Cloud Security & Infrastructure Hardening
- Secure Logging & Monitoring
- Performance vs Security Trade-Offs
- Compliance & Regulatory Considerations
- Security Code Reviews with AI
- Building a Security-First Engineering Career
Requirements
- Basic programming knowledge
- Familiarity with backend or web development concepts
- Interest in AI-assisted development workflows
- No prior deep security expertise required
