AI Security in Six Easy Pieces

Making sense of AI Security for Execs

Remember: S.H.I.E.L.D

• Supply Chain (Rigorous Vendor Due Diligence & Secure the Supply Chain)

• Human Oversight (Prioritize Human Oversight & Training)

• Isolate & Limit (Using Least Privilege)

• Examine & Govern (Implement Robust Data Governance and Control)

• Listen & Monitor (Establish Comprehensive and Continuous Monitoring)

• Defend with Layers (Use multiple security controls to secure your data)

More explanation below:

I've written ten articles examining AI security threats from an executive perspective. One question remains: what should you actually do about it?

Follow six fundamental practices that spell out S.H.I.E.L.D.

If you do, you'll be ahead of 90% of your competitors who are still treating AI security as an afterthought.

Here are just a few thoughts on each topic:

S: Supply Chain

Vet your vendors! I assume your procurement team already knows how to evaluate software vendors, but AI introduces new risks they haven't seen before. You need to verify how models are trained, where training data originates, and what safeguards exist against model tampering.

Security test during UAT: Sandbox test models, libraries, and tools in isolated environments that mirror your production systems. This slows deployment, but the alternative is discovering vulnerabilities after they're embedded in customer-facing applications.

Maintain an asset inventory that tracks every AI component with the same rigor you apply to financial assets. Maintain detailed records of model versions, training data sources, vendor relationships, and security audit results. When vulnerabilities are discovered—and they will be—you need to know exactly what's affected and how quickly you can respond.

H: Human Oversight

Require expert validation for all AI-generated outputs that influence business decisions. Have qualified humans review outputs before they reach customers or inform critical decisions.

Train employees to recognize AI limitations and hallucinations. Your people need practical skills for validating AI outputs and clear guidelines about when human intervention is required.

Treat AI content as untrusted data requiring validation. This is critical for customer-facing applications where incorrect information creates liability issues.

I: Isolate & Limit

Restrict access permissions to only the data and resources required for specific tasks. Your customer service AI doesn't need access to financial databases.

Separate environments that isolate AI systems from critical business data in dedicated computing environments. Run AI workloads in separate cloud accounts with limited connectivity to production.

Implement independent authorization checks that don't rely on AI systems to make access control decisions about their own permissions. Include kill switches for immediate shutdown when anomalies are detected.

E: Examine & Govern

Classify all corporate data before any AI initiatives begin. Establish clear taxonomies distinguishing public, internal, confidential, and restricted information with specific handling requirements.

Manage vector databases with stricter controls than traditional databases because embeddings can inadvertently expose sensitive information through similarity searches and inference attacks.

Track data provenance to maintain detailed records of where information originates, how it's processed, and who has accessed it. When AI systems generate problematic outputs, trace the information back to its source.

L: Listen & Monitor

Monitor inputs and outputs for patterns indicating attempted manipulation, data extraction, or system abuse. Track unusual query patterns and outputs containing sensitive information.

Analyze behavior patterns to establish baselines for AI system performance and alert on deviations that might indicate attacks, data poisoning, or compromise.

Track resource consumption including computational costs, query volumes, and system utilization for signs of denial-of-service attacks or unbounded consumption.

D: Defend with Layers

Implement rate limiting to prevent rapid-fire attacks designed to overwhelm systems or extract large amounts of information through automated queries.

Require approval workflows for sensitive operations that need human authorization before AI systems can access restricted data or perform high-risk operations.

Deploy encryption and access controls that protect data both in transit and at rest, with separate encryption keys for different data categories and strong authentication requirements.

Making S.H.I.E.L.D. Work

These six practices work together to create comprehensive AI security without paralyzing innovation. The framework is designed to be practical: each component addresses real threats while remaining implementable in actual business environments.

Start with data governance and vendor due diligence—these provide the foundation for everything else.

A note here: my friend and colleague, Derrick Jackson, runs Tech Jacks Solutions, a firm specializing in AI Governance. They have a comprehensive framework here.

You can deploy AI quickly, but securely. Organizations that get this balance right will dominate their markets. Those that don't should budget some time and money for incident response.

The choice, as they say, is yours.