April 29, 2024
3 minute read
Specializing in the robust aspects of cybersecurity and artificial intelligence (AI), AI Security Engineers are uniquely positioned to address the increasingly prevalent threats to AI systems and data. AI Security Engineers are responsible for protecting AI systems from various threats, including unauthorized access, malicious attacks, and data breaches. These cybersecurity professionals are also tasked with ensuring that AI systems are developed and deployed in a secure and compliant manner, adhering to industry regulations and best practices.
AI Security Engineer as a Cybersecurity Expert
AI Security Engineers are deeply rooted in the field of cybersecurity. They possess a comprehensive understanding of cybersecurity principles, tools, and techniques and can apply them within the realm of AI. This expertise enables them to identify and mitigate security vulnerabilities in AI systems and develop effective security strategies to protect against cyber threats.
Technical Expertise in AI and Machine Learning
AI Security Engineers not only require cybersecurity knowledge but also a strong foundation in AI and machine learning (ML) concepts. They must understand how AI algorithms work, how to train and deploy ML models, and how to leverage AI techniques for security purposes. This technical expertise allows them to analyze AI systems for potential security flaws and develop AI-powered security solutions.
Day-to-Day Responsibilities of an AI Security Engineer
The day-to-day responsibilities of an AI Security Engineer may include:
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Find a path to becoming a AI Security Engineer. Learn more at:
OpenCourser.com/career/vrbmv7/ai
Reading list
We've selected some books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
AI Security Engineer.
Yevgeniy Vorobeychik and Murat Kantarcioglu formalize adversarial machine learning as a game-theoretic interaction between defenders and adaptive attackers. Rigorous coverage spans evasion attacks, data poisoning, and provable robustness guarantees.
Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, and J. D. Tygar synthesize foundational research on securing machine learning models against intelligent adversaries. Detailed mathematical analyses cover statistical learning under attack, robust optimization, and game-theoretic defenses.
Drawing on real-world red-teaming experience at Microsoft and Harvard, Ram Shankar Siva Kumar and Hyrum Anderson demystify model extraction, backdoor poisoning, and physical adversarial patches. Security leaders and engineers gain practical threat-modeling playbooks and incident response strategies.
Bridging applied data science and defensive engineering for O'Reilly, Clarence Chio and David Freeman demonstrate both how to build anomaly detectors and how attackers evade machine learning classifiers. Python examples walk through adversarial sample crafting, poisoning defenses, and production hardening.
Investigating fundamental theoretical limits on verifying, explaining, and containing advanced autonomous systems, Roman Yampolskiy synthesizes cybersecurity failure modes with AI safety proofs. Readers confront why traditional software verification breaks down when applied to high-dimensional neural networks.
John Sotiropoulos walks MLSecOps engineers through hands-on threat modeling, adversarial attack simulations, and layered defense pipelines for production AI. Code examples cover evasion, poisoning, model extraction, and automated red-teaming workflows.
Dan Hendrycks synthesizes technical AI safety, catastrophic risk analysis, and adversarial robustness into a structured university-level curriculum. Core chapters examine specification gaming, deceptive alignment, interpretability, and systemic security governance.
Sunil Gregory and Anindya Sircar outline enterprise governance architectures for securing generative AI deployments against operational, privacy, and compliance risks. Practical frameworks align risk assessments, model auditing, and incident response with global regulations.
Murat Kuzlu and Ferhat Ozgur Catak examine adversarial vulnerabilities across cyber-physical and wireless AI systems, pairing theoretical threat models with defensive countermeasures. Case studies highlight robust sensing, secure edge inference, and privacy-preserving learning.
Cynthia Dwork and Aaron Roth establish the mathematical foundations of differential privacy, proving how rigorous noise calibration prevents training-data reconstruction and membership inference attacks. Indispensable formal tools protect sensitive datasets in machine learning.
Ross Anderson's encyclopedic third edition anchors AI security within broader cryptographic, protocol, and socio-technical threat modeling, including dedicated coverage of machine learning attacks and biometric spoofing. Every AI security architect needs its systems-level perspective on incentives and failure modes.
Structured frameworks like STRIDE, attack trees, and data-flow diagrams developed by Adam Shostack remain the bedrock of modern AI red-teaming and MITRE ATLAS threat assessments. Applying systematic threat modeling early in ML pipeline design prevents architectural blind spots.
Bridging explainability, robustness, and security engineering, this technical volume analyzes how statistical vulnerabilities expose deployed machine learning models to manipulation. Readers gain structured evaluation criteria for hardening high-stakes decision systems.
Examining cyber-physical systems where software vulnerabilities translate into kinetic real-world harm, Bruce Schneier frames the regulatory and resilience challenges of autonomous vehicles, robotics, and smart infrastructure. AI engineers gain vital context on safety-critical deployment risks.
Addressing the full stack of foundation model vulnerabilities, Ken Huang details indirect prompt injection, RAG vector database poisoning, agentic tool-use exploits, and guardrails architecture. Enterprise architects gain actionable blueprints for securing LLM applications in production.
For more information about how these books relate to this course, visit:
OpenCourser.com/career/vrbmv7/ai
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