[COURSE] Why GenAI Security Fails: Lessons for CISOs

Avoid these mistakes when you are trying to secure your GenAI applications

I had a recent catchup with two colleagues of mine in the industry both of whom are Chief Information Security Officers (CISOs).

Interestingly enough both of them were struggling to secure GenAI systems within their company

No big surprise given that Gartner predicts over 80% of companies will have GenAI deployed in form or another by 2026

With such rapid adoption, the CISO’s role in making sure GenAI does not cause a data breach becomes even more critical

I see a lot of CISOs adopting one of the two extremes below

Bad news is neither of them work ..

• Banning GenAI is not the solution as businesses see it as a massive competitive advantage

• Allowing GenAI without any control means you could soon see your company’s name in the headline after a massive data breach

Additionally there are some other common mistakes that I see CISOs make with GenAI security.

1. Underestimating The Unique Risks of GenAI

GenAI is not just another application

Unlike traditional IT systems, GenAI introduces new attack vectors such as:

• Prompt Injections: Malicious actors manipulating AI prompts to alter outputs or expose sensitive data.

• Data Poisoning: Adversaries corrupting training data to influence model behavior.

• Hallucinations: AI generating false or misleading information that could harm decision-making processes.

CISOs often fail to recognize these risks early, which leaves their organizations exposed.

The rapid pace of GenAI adoption means that many security teams are still playing catch-up, leaving gaps in threat modeling and risk assessments.

Without understanding these unique challenges, organizations cannot develop effective security strategies tailored to GenAI.

2. Over-Emphasis on Security Tooling

The allure of the next shiny security tool can sometimes overshadow the need for a comprehensive strategy.

CISOs may rely too heavily on tools for monitoring and intrusion detection, assuming these will be sufficient to secure GenAI systems.

While tools are valuable, they are not a substitute for a well-rounded approach that includes:

• Governance Policies: Defining how GenAI systems are accessed, used, and monitored.

• Data Protection Measures: Ensuring that sensitive data used for training or interacting with the model is adequately safeguarded.

• Continuous Risk Assessments: Regularly evaluating the threat landscape as GenAI systems evolve.

By focusing disproportionately on tools, CISOs risk neglecting other critical elements of security, such as user education, governance frameworks, and proactive threat modeling.

3. GenAI Security Strategies Are Either Too High-Level or Too Low-Level

Striking the right balance between high-level governance and low-level technical implementation is a massive challenge

Some CISOs focus exclusively on high-level strategies, such as setting overarching AI ethics policies, without providing actionable guidelines for their implementation.

Others dive too deeply into technical aspects — like model encryption — without addressing broader governance issues such as:

• How to handle third-party GenAI APIs.

• Establishing accountability for AI-generated decisions.

• Managing regulatory compliance for industries like healthcare or financial services.

An unbalanced approach creates gaps in security coverage, leaving systems either too rigid to adapt to new threats or too vague to be actionable.

The Right Way — GenAI Security Frameworks

To address these challenges, CISOs must adopt structured security frameworks that provide actionable guidance for securing GenAI systems.

There are many GenAI security frameworks present but two key ones gaining traction are:

1 — MITRE ATLAS

A knowledge base designed to help organizations understand, mitigate, and respond to adversarial threats in AI systems.

It provides detailed guidance on identifying and addressing specific risks like data poisoning, adversarial inputs, and model evasion attacks.

A great resource for threat modeling GenAI systems.

https://atlas.mitre.org/matrices/ATLAS

2 — AWS Generative AI Security Scoping Matrix

My personal fav framework when it comes to GenAI.

This framework focuses on securing GenAI applications deployed in AWS environments.

It emphasizes controls such as data encryption, access management, and monitoring to ensure the security of both the underlying models and their outputs.

These frameworks not only offer practical tools and methodologies but also help CISOs align their security strategies with industry standards, ensuring consistency and comprehensiveness.

https://aws.amazon.com/blogs/security/securing-generative-ai-an-introduction-to-the-generative-ai-security-scoping-matrix/

The Way Forward

As GenAI becomes a cornerstone of modern businesses, securing these systems is no longer an option.

The unique risks associated with GenAI, coupled with its rapid adoption, demand a shift in how CISOs approach security.

If your company has started its GenAI journey then you need to upskill and start implementing these GenAI security frameworks today

Still interested ?

Then check out my latest course on "Securing GenAI Systems with Best Practice Frameworks" below !

This course provides a comprehensive guide to understanding, assessing, and implementing robust security measures for Generative AI systems.

It explores key frameworks and methodologies, including Google SAIF and AWS Generative AI Scoping Matrix, empowering you to secure GenAI applications effectively.

What You Will Learn

• Fundamental principles and best practices for securing GenAI systems.

• Insights into common pitfalls in Generative AI security and strategies to avoid them.

• A deep dive into security frameworks like Google SAIF and AWS Generative AI Scoping Matrix.

• Implementation of security controls tailored for GenAI applications.

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