Turning best practices into daily engineering habits.

Secure by Design: Building an AI Security & Safety Program

Turning best practices into daily engineering habits

From “Cool Model” to Critical System

In many organizations, AI started as innovation experiments; now, models are embedded in customer journeys, decision workflows, and safety-critical systems. NIST and ENISA both emphasize that AI security and safety can’t be treated as an afterthought—they must be integrated into the development lifecycle and operational playbooks. NIST Publications+2ENISA

The shift mirrors what DevSecOps did for software: security becomes everyone’s job, from data engineers and ML scientists to product managers and compliance teams.

A Secure AI Development Lifecycle (AI-SDLC)

A practical AI-SDLC weaves security and privacy steps into every stage:

• Design: Threat model data flows, attack surfaces (APIs, prompts, training pipelines), and misuse scenarios before any code or labeling starts. ENISA

• Build: Apply secure coding standards to data pipelines and training code, enforce secrets management, and use reproducible training with signed model artifacts.

• Test: Combine standard QA with adversarial testing, robustness evaluation, and red team exercises focused on prompt injection, poisoning, and model evasion.

Documentation of these steps doubles as evidence for regulators and auditors, especially under high-risk categories in the EU AI Act. ISACA

Continuous Monitoring and Incident Response for AI

Deployed models drift, attackers adapt, and data changes. That’s why modern AI security is moving toward continuous assurance:

• Behavioral monitoring for unusual input patterns and output anomalies (e.g., sudden spikes in certain prompts or classes).

• Automated retraining or rollback pipelines when performance or fairness thresholds are breached.

• AI-aware incident response runbooks that define when a model should be quarantined, retrained, or taken offline. NIST

This is also where human oversight becomes real—not just a checkbox—by empowering operators to override or pause AI behavior when red flags appear.

Training People, Not Just Models

Technical controls fail if humans don’t understand them. Leading organizations are investing in AI literacy for:

• Security teams: To recognize AML patterns, evaluate AI vendor claims, and design relevant detections. NIST Computer Security Resource Center

• Data scientists and ML engineers: To integrate privacy-preserving methods, apply DP or anonymization correctly, and design robust evaluation suites. arXiv

• Business stakeholders: To understand when and why to treat an AI deployment as “high risk” and insist on proper governance.

Security champions embedded in ML teams can bridge these worlds, turning policies into pragmatic guidance, templates, and reusable code.

Closing Thoughts and Looking Forward

A mature AI security and safety program will look a lot like modern software security—only broader: it has to care about rights, fairness, and societal impact, not just outages and breaches. Over time we’ll likely see:

• Standard AI-SDLC playbooks shared across industries, with sector-specific controls for healthcare, finance, and public sector. NIST

• Certification and assurance schemes (possibly tied to EU AI Act conformity assessments) that recognize well-run AI security programs. Artificial Intelligence Act

• Automation-first governance, where policies and guardrails are enforced directly through code, pipelines, and infrastructure-as-code templates.

Done well, AI security and safety don’t slow innovation—they make it repeatable, auditable, and worthy of the trust users place in intelligent systems.

Reference Sites

1. Artificial Intelligence Risk Management Framework (AI RMF 1.0) – NIST
   https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf NIST Publications

2. Artificial Intelligence – How to Make Machine Learning Cyber Secure – ENISA
   https://www.enisa.europa.eu/news/enisa-news/artificial-intelligence-how-to-make-machine-learning-cyber-secure ENISA

3. Securing Machine Learning Algorithms – ENISA
   https://complexdiscovery.com/wp-content/uploads/2021/12/ENISA-Report-Securing-Machine-Learning-Algorithms.pdf ComplexDiscovery

4. Defense Strategies for Adversarial Machine Learning – Computer Science Review
   https://www.sciencedirect.com/science/article/abs/pii/S1574013723000400 ScienceDirect

5. Cybersecurity, Privacy, and AI – NIST
   https://www.nist.gov/itl/applied-cybersecurity/cybersecurity-privacy-and-ai NIST

Author: Serge Boudreaux – AI Hardware Technologies, Montreal, Quebec
Co-Editor: Peter Jonathan Wilcheck – Miami, Florida