As organizations rush to embrace generative AI, a critical gap is emerging between adoption and protection. New research from Cobalt reveals a growing disconnect: 98% of organizations are incorporating AI into their products, but only 66% are conducting regular security assessments. In a world where AI-generated content and behavior are increasingly driving customer experience and business decisions, that’s a dangerous oversight.
Welcome to the AI security blind spot—an area of fast-moving innovation that’s largely invisible to today’s security tools, teams, and frameworks.
A Perfect Storm: Rising Risks, Inadequate Defenses
The Cobalt study paints a troubling picture. Seventy-two percent of security leaders now identify generative AI attacks as their top IT risk. Yet more than one-third of organizations aren’t performing regular security assessments on their AI systems. Thirty-six percent openly admit that demand for generative AI has outpaced their ability to manage its risks.
Why are security teams falling behind? As Mali Gorantla, Chief Product & Security Officer at PointGuard AI, explains: “The explosion of GenAI technologies has introduced an entirely new class of risks that traditional tools and processes weren’t designed to address. Without clear ownership or tested frameworks, security teams are playing catch-up.”
Several forces are converging:
- Speed over security. AI features are often launched under business pressure, with little time to apply meaningful security scrutiny.
- Legacy tools fall short. Traditional scanners, firewalls, and detection tools weren’t built for the unique attack surfaces of LLMs and GenAI.
- Expertise gaps. AI security is still a nascent discipline. Few security teams have the in-house knowledge to secure large language models (LLMs), protect against prompt injection, or evaluate model supply chains.
- Regulatory lag. Compliance frameworks are unclear or still forming. Many organizations don’t know what they’re accountable for—so they underinvest.
As Gunter Ollmann, CTO at Cobalt, warns:
“The current trajectory is unsustainable, but it is reversible. Security leaders must take decisive action to close this gap.”
Understanding the LLM Blind Spot
What exactly makes LLMs so different—and dangerous?
Unlike traditional software, LLM behavior is driven by dynamic input and probabilistic responses. These models can be manipulated in ways that are difficult to predict, test, or monitor. The most prominent example is prompt injection—where attackers craft inputs that subvert the model’s intended behavior, often leading it to disclose sensitive data, make unauthorized API calls, or produce harmful content.
But that’s just the beginning. Other attack vectors include:
- Training data poisoning
- Model extraction
- Identity spoofing
- Overreliance on third-party models and datasets
This new category of vulnerabilities operates outside the scope of traditional DevSecOps and AppSec processes, and often outside the awareness of the teams who need to manage it.
Closing the Gap Without Creating Another Silo
It’s tempting to build an entirely new AI security function—but that would be a mistake.
Security teams are already managing fragmented tools and alerts across cloud, endpoint, identity, and more. Adding another silo risks more overhead, more noise, and less visibility.
Instead, the right move is to evolve existing security practices to include AI as a first-class concern. That means:
- Developing controls specific to LLM behavior
- Extending DevSecOps to support “AISecOps”—a collaborative model between security and AI/ML teams
- Conducting regular red teaming, threat modeling, and response exercises for AI components
- Managing the AI supply chain with the same rigor as software dependencies and cloud configurations
In short, we need new protections—but they must integrate, not isolate.
How PointGuard AI Can Help
PointGuard AI was built specifically to close the AI security readiness gap. Our full-stack platform offers a unified approach to governing, hardening, and defending AI systems—without adding complexity to your existing security operations.
Here’s how:
Automatically identifies and inventories LLMs, vector stores, prompts, and embedded AI components across your environment—so nothing operates in the shadows.
Applies secure defaults and protection layers against prompt injection, sensitive data exposure, model override, and hallucination risks.
Continuously tests AI systems using adversarial techniques to identify exploitable behavior before attackers do.
Monitors LLM interactions in real-time to detect prompt abuse, malicious input chaining, and anomalous responses.
Data Protection
Implements guardrails to prevent leakage of sensitive or proprietary information through AI outputs or embeddings.
Integrated with Your Full Application Stack
PointGuard AI provides seamless connection between DevSecOps and AISecOps, protecting the full AI-to-application stack with a unified platform.
From development to production, from compliance to defense, PointGuard AI is your safety net for AI innovation.
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