Page 90 - Cyber Defense eMagazine September 2025

P. 90

AI Risks Are Unlike Traditional Threats

Traditional security frameworks were designed for deterministic systems with fixed logic and understood
failure modes. AI systems, especially those based on deep learning, behave differently. They evolve over
time, generate outputs based on statistical associations, and often lack clear reasoning behind their
decisions. This makes the output of AI systems difficult to validate, monitor, and govern using legacy
methods.

For example, when AI systems are trained on biased data, they can make discriminatory decisions even
if the code itself seems fine. Generative models might create content that looks accurate but is actually
false. Once these systems are built into everyday operations, they can quietly disrupt performance,
violate ethical standards, or create legal risks across the organization.

Compounding the problem is speed. AI adoption is outpacing governance maturity in most organizations.
Business units experiment with third-party tools, developers prototype with unvetted models, and
leadership teams greenlight AI initiatives with limited visibility into risk implications. Security and
assurance teams are often brought in after deployment, too late to influence design or validate
safeguards. This results in a fragmented ecosystem where risk accumulates quietly and explosively.

Prompt Injection and Semantic Exploits

Unlike traditional software, AI models interpret natural language. This creates an entirely new form of
prompt injection attack. By embedding malicious or manipulative instructions into user-facing inputs,
attackers can bypass policies or trigger unintended behavior in AI systems.

Consider a scenario where a generative assistant is integrated with enterprise workflows. If an attacker
enters a prompt that mimics internal authority or overrides prior instructions, the model may approve
transactions, escalate tickets, or disclose sensitive information. These systems interpret meaning, not
syntax, which makes conventional input validation ineffective.

Prompt injection exploits are difficult to detect because they look like normal conversations. They blend
into the flow of communication, relying on the model’s willingness to comply with what appears to be a
legitimate request. Security teams must begin thinking about inputs not just as data, but as potential
command surfaces. Defending against this class of attacks requires runtime controls, model constraints,
user education, and forensic logging at the prompt level.

Synthetic Media and the Collapse of Trust

AI generated content, like deepfakes, voice clones, and fake documents, is forcing us to rethink identity,
communication, and trust. It’s now easier than ever to impersonate leaders, spread false information, and
manipulate narratives. The result is a growing confusion between what’s real and what’s fabricated, not
just within organizations but across public conversations as well.

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