Page 91 - Cyber Defense eMagazine September 2025

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What makes synthetic media dangerous is not just its realism. It is the ease and scale with which it can
be produced and distributed. A deepfake voice call or video message can now be generated in minutes
and deployed in social engineering campaigns that bypass technical defenses. The result is a
fundamental erosion of confidence in audio, visual, and textual communication.

Enterprise security must now account for the authenticity of messages, not just their source. This means
putting media verification tools in place, setting up separate channels for secure authentication, and
having a clear crisis response plan ready in case of synthetic impersonation. Training also needs to go
further, teaching employees and stakeholders how to spot fake or manipulated content before it can do
real damage.

Shadow AI

Across organizations, employees are using AI tools outside of sanctioned environments. They paste
proprietary content into generative interfaces, rely on code written by AI without validation, and use free
third-party models to make business decisions. This shadow AI phenomenon is growing rapidly and
silently.

The risks are significant. Sensitive data may be stored or reused by external providers. AI generated
outputs may contain hallucinated content or introduce security vulnerabilities. Decision logic may vary
across teams, leading to inconsistent and biased outcomes.

The answer isn’t to ban AI completely. Instead, organizations need to build safe environments where
innovation can happen with the right oversight and controls. That starts with using approved AI platforms,
setting clear policies based on real business needs, and closely monitoring how AI is being used to catch
any issues early. Security teams should see their role not just as gatekeepers, but as partners in helping
the organization use AI in a smart, responsible way.

Model Inversion and Extraction

AI models are no longer just tools running quietly in the background. They can now be poked, prodded,
and manipulated through the same public interfaces meant for users. Attackers are finding ways to
reverse-engineer these models, pull insights about their training data, or even copy their core functionality
outright.

The consequences are serious. These types of attacks can lead to data breaches, stolen intellectual
property, or a noticeable drop in model performance. What makes it worse is that traditional security tools
often miss them. From the outside, the model still appears to work normally, so no alarms are triggered.

To stay secure, organizations need to treat AI systems as core components of their security environment.
This involves putting clear safeguards in place, like managing who has access, limiting how often the
systems can be used, applying privacy-focused techniques, and closely monitoring for any suspicious
user activity. It's also important to go deeper than just the application layer. Security checks should
include the AI model itself, since it can be a potential target for attacks.

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