AI is no longer just a feature—it’s infrastructure.

As LLMs, agents, and generative systems are embedded into everything from customer service to legal ops, we’re entering a new security era.

But here’s the problem:

AI systems are being deployed faster than they’re being secured. Traditional cybersecurity frameworks weren’t built to handle things like prompt injection, agent drift, or probabilistic model behavior.

That’s why I wrote:

“Why LLMs, Agents, and AI Systems Need Dedicated Security Frameworks.”

This white paper explores:

Why AI is the next major attack surface

Real-world breaches you might’ve missed (Samsung, OpenAI, Bing/Sydney)

How to embed AI-specific controls into your existing stack

Regulatory moves that will impact how we build and audit AI systems

AI security isn’t just a niche—it’s the next evolution of cybersecurity.

We can’t defend neural networks with frameworks built for networks.

Why LLMs, Agents, and AI Systems Need Dedicated Security Frameworks

Executive Summary

AI is no longer just a feature—it's becoming infrastructure. Large language models (LLMs), autonomous agents, and generative AI tools are being embedded in customer service, analytics, coding, legal work, and more.

But while adoption accelerates, security practices haven’t caught up.

This whitepaper explains why AI security is the next evolution of cybersecurity, what threats enterprises face, and how to integrate AI-specific controls into your existing security architecture.

From Networks to Neural Nets: The Next Attack Surface

In the early 2000s, businesses scrambled to secure websites and cloud apps. Today, we’re facing a similar inflection point with AI as a new digital layer.

AI-Specific Threats Enterprises Face

According to ENISA's 2023 AI Threat Landscape, AI systems face distinct attack vectors, not covered by traditional security controls.

Real-World Incidents

OpenAI API Bug (2023): A caching issue exposed other users’ prompts and payment info.
Samsung Source Code Leak (2023): Engineers unknowingly shared IP with ChatGPT.
Bing Chat/Sydney Jailbreaks: Demonstrated how LLMs can be coerced to bypass safety filters.

Why Traditional Cybersecurity Isn’t Enough

LLMs introduce probabilistic behavior, making them harder to secure with deterministic tools.

Integrating AI into Cybersecurity Programs

Expand Security Architecture:

Add AI observability tools for real-time tracing
Use prompt firewalls (e.g., sector8.ai's SDKs)
Treat prompts like code or transactions

Update Risk Registers:

Include LLM prompt injection, agent drift, and shadow AI in threat models

Train the SOC:

AI-specific indicators of compromise (token anomalies, abuse patterns)
Integrate with SIEMs (Splunk, Sentinel) to flag LLM risks

Regulatory & Standards Alignment

By 2026, Gartner predicts 70% of enterprises will be required to audit AI use for compliance

Recommendations for Security Leaders

Final Word

AI is the new attack surface.

From prompt manipulation to compliance exposure, enterprises must evolve their security mindset—and their stack. Treating AI security as an extension of cybersecurity is no longer enough. It’s time to give it first-class status.

References

ENISA AI Threat Landscape 2023
OWASP Top 10 for LLM Applications
MITRE ATLAS for Adversarial AI
NIST AI RMF
Gartner: Strategic Roadmap for Generative AI Security, 2024