InfoQ Opens AI Security & Privacy Engineering Cohort for Regulated Industries

2026-07-06 · Source: InfoQ · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

InfoQ has launched its AI Security & Privacy Engineering Program, a five-week online cohort designed for senior engineers and architects managing AI system security and privacy in regulated industries. Two cohorts are scheduled, starting August 26 and October 14, each limited to practitioners with at least five years of experience. Sessions run four hours weekly, facilitated by Katharine Jarmul, author of *Practical Data Privacy* (O'Reilly). The program, costing USD 1,470, covers critical areas including sensitive data handling, threat modeling with methods like STRIDE and LINDDUN, hands-on red teaming, implementing controls and sandboxes, and observability using tools like Arize Phoenix. Participants also learn governance and auditing, culminating in a documented risk assessment and mitigation report for an AI product architecture. This initiative addresses the current lack of external peer review in AI security work.

Key takeaway

For AI Security Engineers and Architects in regulated sectors, this program offers a structured approach to navigating complex AI privacy and security challenges. You will gain practical frameworks and peer insights to identify risks, implement effective controls, and establish robust governance for your AI systems. Consider enrolling in a cohort to enhance your ability to build trustworthy AI architectures and ensure compliance, moving beyond vendor assumptions.

Key insights

AI security and privacy in regulated industries require structured frameworks and peer-reviewed decision-making to build trust.

Principles

• Trust in AI hinges on robust privacy and security.
• Don't assume vendors handle all compliance.
• Address information security basics early.

Method

Apply QCon frameworks to real-world security/privacy decisions, discuss outcomes with peers, then produce a documented risk assessment and mitigation report.

In practice

• Identify AI architecture risks.
• Use STRIDE, LINDDUN for threat modeling.
• Implement controls and sandboxes.

Topics

• AI Security
• Data Privacy
• Regulated Industries
• Threat Modeling
• AI Governance
• Red Teaming

Best for: AI Security Engineer, AI Architect, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by InfoQ.