Round Table Discussion Points on AI Security

AI Security Starts with the Basics

I was recently invited to join a round‑table discussion on AI and security concerns. As you can imagine, the list of risks is long and growing. But for this post, I want to stay grounded in the fundamentals—the security controls that mattered long before AI and matter even more now.

And yes, in true modern fashion, I often draft my thoughts in near‑ready form and run them through an AI system for grammar checks and polish. That’s not a shortcut; it’s a workflow. This blog isn’t meant to be a step‑by‑step guide. It’s meant to spark thought, encourage your own research, and remind you that the basics still matter.

The “Old Reliable” Security Controls Still Matter

Let’s start with three foundational controls that didn’t suddenly appear because of AI—they’ve always been essential.

1. Securing the Data: The Foundation of Every AI System

AI systems are only as secure as the data they’re trained on and the data they access. If the underlying data layer is weak, everything built on top of it inherits that weakness.

Core practices include:

• Hardening databases and vector stores

• Encrypting data at rest and in transit

• Monitoring for data poisoning attempts

• Filtering malicious inputs before they enter the pipeline

• Implementing strict access controls and audit logging

Why it matters:
AI models can be subtly manipulated through poisoned or corrupted data. A single malicious dataset can shift model behavior in ways that are hard to detect but impactful over time.

2. Securing AI Service Accounts: The Overlooked High‑Risk Area

Diagram by: Octree | IAM www.actree.co.uk

AI systems often run with elevated privileges, broad access, or automated decision‑making authority. That makes their service accounts prime targets.

Core practices include:

• Strong authentication with no static keys

• Least‑privilege access for model, data, and downstream actions

• Credential rotation and monitoring

• Preventing privilege escalation by AI agents

Why it matters:
If an attacker compromises an AI service account, they may gain:

• Access to sensitive data

• Ability to manipulate model outputs

• Ability to inject malicious training data

• Ability to impersonate the AI system

This is one of the highest‑risk areas in AI security today—and one of the most frequently overlooked.

3. Secure Coding for AI Developers: Same Principles, New Threats

If your developers already follow secure coding practices, great. But AI introduces new classes of vulnerabilities that traditional training doesn’t cover.

Core practices include:

• Training developers on prompt injection

• Understanding model‑specific vulnerabilities (jailbreaks, leakage, etc.)

• Sanitizing inputs before they reach the model

• Validating outputs before they reach downstream systems

• Using secure deployment frameworks

• Ensuring supply‑chain security for libraries and model weights

Why it matters:
Developers must understand:

• How LLMs can be manipulated

• How to prevent model inversion attacks

• How to stop prompt‑based privilege escalation

• How to secure model endpoints

AI expands the attack surface, and developer awareness must expand with it.

Beyond the Basics: What Modern AI Security Programs Must Include

Once the fundamentals are in place, AI‑era security programs should also account for:

• Model security to protect against:

  • Model theft

  • Model inversion

  • Membership inference

  • Adversarial examples

• Prompt and input security to prevent:

  • Direct prompt injection

  • Indirect prompt injection

  • Output manipulation

• Output validation to ensure AI cannot:

  • Execute harmful actions

  • Leak sensitive data

  • Generate unauthorized commands

• AI supply‑chain security to ensure:

  • Model weights are authentic

  • Training data sources are trusted

  • Dependencies are secure

• Monitoring and incident response tailored for AI—because AI failures don’t look like traditional system failures.

Final Thoughts

Hopefully, this highlights an area or two that may not have been on your radar before. And if nothing here was new to you, then you are already off to a strong start.

The important thing to remember is that AI did not eliminate the need for foundational security practices. In many ways, it amplified their importance.

The organizations that will succeed in securing AI systems are not necessarily the ones chasing every new security trend. They are the ones consistently applying strong fundamentals while adapting thoughtfully to the unique risks AI introduces.