🛡️ CISO PLAYBOOK • AI GOVERNANCE & RISK

The AI Security Checklist: 5 Strategic Questions to Ensure Your Solution Doesn’t Become Your Biggest Risk

By CyberDudeBivash • October 07, 2025 • Strategic Pillar Post

cyberdudebivash.com | cyberbivash.blogspot.com

Disclosure: This is a strategic guide for security and technology leaders. It contains affiliate links to relevant training and enterprise security solutions. Your support helps fund our independent research.

The 5 Strategic Questions for AI Security:

What Data Is It Trained On, and What Data Is It Learning From?
How Do We Control What It Can Access?
How Are We Protecting It From Prompt Injection?
Who Owns the Output, and Where Does It Go?
How Do We Monitor and Audit Its Actions?

Generative AI is a double-edged sword. It offers the potential for unprecedented productivity gains, but it also introduces a new and poorly understood attack surface. Deploying AI without a robust security and governance framework is not just a technical risk; it is a critical business risk. Before your organization integrates any new AI solution, your security and leadership teams must have a clear answer to these five strategic questions.

Question #1: What Data Is It Trained On, and What Data Is It Learning From?

This question addresses two fundamental risks: the supply chain and the data integrity.

Supply Chain Risk:** Are your teams downloading pre-trained models from public repositories? If so, you are facing a massive supply chain risk. As we detailed in our report on **the ‘Trojan Horse’ of AI**, these models can be backdoored to execute malicious code.
**Data Poisoning Risk:** If your AI model is continuously learning from new data, how do you prevent an attacker from intentionally feeding it bad information to manipulate its future decisions and outputs?

Question #2: How Do We Control What It Can Access?

This is the central security question for **Autonomous AI Agents**. If you give an AI agent a standing, highly privileged API key to your entire cloud environment, you are creating a single point of catastrophic failure. A successful prompt injection attack would instantly give the adversary full control. A **Zero Trust** model is essential. Agents must be granted the absolute minimum permissions necessary (Least Privilege) and should use ephemeral, short-lived credentials for each specific task (Just-in-Time Access).

Question #3: How Are We Protecting It From Prompt Injection?

Prompt injection is the #1 vulnerability in all LLM-based applications. You must assume that attackers will try to inject hidden commands into every single piece of untrusted data your AI processes, whether it’s an email, a PDF, or a webpage. Your architecture must include robust input sanitization and output validation to detect and neutralize these hijacking attempts. Treat all inputs to the LLM as hostile.

Question #4: Who Owns the Output, and Where Does It Go?

This is a critical data governance and legal question. If an employee uses a public AI service and inputs confidential corporate data, where does that data go? Who owns the output? You must have clear policies and technical controls to prevent the leakage of proprietary information into public models. This is the core of the **’Shadow AI’** problem.

Question #5: How Do We Monitor and Audit Its Actions?

You cannot secure what you cannot see. An AI agent is a new type of actor on your network, and its every move must be monitored. Every API call it makes, every tool it uses, and every decision it makes must be logged in a centralized and tamper-proof manner. This telemetry is useless in a silo; it must be fed into your central **XDR or SIEM platform** where your security team can use behavioral analytics to hunt for anomalous or malicious activity.

Fight AI with AI: Detecting the anomalous behavior of a compromised AI agent requires an AI-powered defense. An XDR platform like **Kaspersky’s XDR** uses its own machine learning to build a baseline of normal activity and can automatically alert on the subtle deviations that signal a hijacked agent.

Get CISO-Level AI Security Intelligence

Subscribe for strategic analysis of AI security, governance, and risk management. Subscribe

About the Author

CyberDudeBivash is a cybersecurity strategist with 15+ years in AI security, cloud architecture, and risk governance, advising CISOs across APAC. [Last Updated: October 07, 2025]

#CyberDudeBivash #AISecurity #CISO #Checklist #Governance #RiskManagement #PromptInjection #LLMSecurity #CyberSecurity #InfoSec

Cyberdudebivash