AI security crisis: Why traditional cybersecurity can’t handle the AI revolution

BrandPost By Jeff Miller
Aug 8, 20254 mins

Organizations are rapidly adopting AI with dangerous security blind spots, as development speed outpaces security teams’ ability to address new threats from AI-generated code.

Credit: Shutterstock/Gorodenkoff

Organizations are rushing headlong into AI adoption with potentially dangerous blind spots in their security strategies. While essentially all companies now embrace AI-assisted application development, according to a survey from Palo Alto Networks, nearly half express concerns about security risks from AI-generated code. This disconnect highlights the inadequacy of traditional cybersecurity approaches for AI systems.

AI developers are making major breakthroughs weekly or monthly, which far outpaces the ability of security teams to develop policies and best practices. Because enterprises are under such immense pressure to rapidly adopt AI to remain competitive, security considerations are often an afterthought, at best.

Compressed timelines make it nearly impossible to perform a thorough risk assessment before AI systems go live. Unlike previous technology transformations, AI adoption relies on simple APIs and supporting frameworks that remove traditional skill barriers, enabling rapid deployment without requiring a high level of security expertise.

The ease of accessing cloud-based AI services through APIs has made it easy to deploy shadow AI projects that lie outside of IT’s knowledge. Development teams without deep AI expertise can now integrate powerful language models into applications, but this accessibility comes at a cost. A lack of expertise means such projects often lack proper security oversight, data governance, and output controls.

Where large language models are utilized,  sensitive internal data may inadvertently be exposed during fine-tuning processes or retrieval-augmented generation implementations. Companies customizing AI for specialized applications like customer support or HR assistance often fail to put up adequate safeguards around the sensitive data these systems access.

AI systems introduce entirely new categories of security threats that existing tools cannot adequately address. One example is data poisoning attacks, where adversaries inject malicious examples into training data to manipulate model behavior. Detecting model evasion techniques and hidden biases that could manipulate AI outputs in harmful ways will require specialized detection approaches.

Unlike traditional software vulnerabilities that can be patched with code changes, AI model issues often require complete retraining from scratch, a process that can take weeks or months to complete and cost hundreds of thousands of dollars in compute resources.

The evolving regulatory landscape further compounds these challenges. Emerging frameworks like the European Union AI Act places new demands on organizations for AI oversight and governance, particularly for high-stakes applications in hiring, credit scoring and law enforcement. Security teams must expand beyond traditional data protection to consider fairness, transparency, and accountability.

Organizations need comprehensive governance frameworks focused on visibility and control. Security teams must maintain inventories of all deployed AI models, track data usage across the AI life cycle, and document capabilities and access permissions. Without this foundational visibility, risk assessment and policy enforcement become impossible.

Success requires abandoning the traditional reactive approach to security. Enterprises must embed security and governance considerations into AI development from the outset, which requires close collaboration between security, legal, and AI development teams.

The stakes are particularly high for public-facing AI applications, which require rigorous bias testing, adversarial testing for safety risks, and more frequent compliance audits. Technical guardrails like rate limiting, content filtering, and automated shutdown triggers based on predefined risk thresholds become essential.

As AI continues its rapid integration into business operations, organizations that fail to proactively address these security challenges risk significant exposure in an increasingly complex threat landscape.

Download the full report for a deeper dive.