Page 197 - Cyber Defense eMagazine RSAC Special Edition 2025

What are the unique challenges organizations face when implementing AI?

Organizations, especially those in education, healthcare and finance face a fundamental security
paradox. They need to be open enough to foster innovation while simultaneously protecting highly
sensitive data, including records protected by FERPA, HIPAA and others.

When organizations implement AI solutions, they're essentially creating new data pathways that weren't
accounted for in traditional security models. GCE encountered this firsthand when implementing their AI
chatbot platform for university students and faculty. Their security team recognized early on that users
might inadvertently share personally identifiable information or sensitive health data that could potentially
be stored in backend systems or used to train future models.

The challenge becomes even more complex because these institutions often operate with limited security
resources but face sophisticated threats. They need security solutions that can scale across thousands
of users without hindering the user experience or technological advancement.

What security risks should CISOs in education and other sensitive industries be most concerned about
when deploying AI?

The primary concern is what I call "unintentional data leakage" – when users share sensitive information
with an AI system without realizing the downstream implications. Unlike traditional applications where
data pathways are well-defined, AI systems can be black boxes that ingest, process and potentially store
information in ways that aren't immediately transparent.

There's also the risk of prompt injection attacks, where malicious actors might try to trick AI systems into
revealing information they shouldn't have access to. And we can't forget about the possibility of AI
systems generating harmful or misleading content if not properly secured.

Organizations need to think about securing both the input to these AI systems – what users are sharing
– and the output – what the AI is generating in response.

How can institutions balance security with the need for technology adoption and accessibility?

A reflexive security response might be to lock everything down, but that approach simply doesn't work. If
security becomes too restrictive, users will find workarounds – they'll use unsanctioned consumer AI tools
instead, creating an even bigger shadow IT problem.

The key is implementing what I call "frictionless security" – protection mechanisms that work in the
background without users even knowing they're there. Think of it as an invisible safety net that catches
sensitive data before it reaches external AI models but doesn't impede legitimate uses.

API-driven security approaches work particularly well here. Rather than restricting AI adoption,
organizations can implement security solutions that automatically redact sensitive information from user
prompts and uploaded files in real-time before they reach external AI models. As Mike Manrod, CISO at
GCE explains: "What Pangea has allowed us to do is to intercept and apply logic to those prompts. If the

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