How runtime attacks can turn profitable AI into budget holes

Understanding the Hidden Financial Risks of Runtime Attacks on AI Systems

In today’s enterprise landscape, the deployment of artificial intelligence (AI) solutions offers significant advantages, but many organizations overlook a critical factor: the hidden security costs associated with AI inference. While AI models can deliver real-time insights and automation, they also introduce new vulnerabilities during their operational phase—often referred to as runtime—that can significantly inflate total ownership expenses and erode expected returns.

The Rising Cost of AI Security Incidents

Operational AI systems, especially within regulated sectors, are increasingly susceptible to costly security breaches. Containment of a single incident can run into millions of dollars, with some cases exceeding $5 million. Additionally, implementing comprehensive compliance measures retroactively can cost hundreds of thousands of dollars, further impacting ROI. Beyond direct expenses, a single trust failure—such as a biased or malicious AI output—can lead to reputational damage, market value decline, or loss of key contracts, transforming AI initiatives into unpredictable financial risks.

Emerging Threats During AI Inference

Cyber adversaries are actively exploiting vulnerabilities present during the inference process—the moment AI models generate outputs. These attack vectors align with well-known security risks highlighted by the OWASP Top 10 for large language models (LLMs). Common tactics include manipulating prompts to inject malicious instructions, poisoning training data to corrupt model behavior, exploiting integration points through supply-chain vulnerabilities or plugins, and extracting sensitive data from outputs. For example, a compromised plugin once exposed access tokens on dozens of servers, illustrating the real-world implications of these weaknesses.

Recent statistics underscore the urgency of addressing inference security. In early 2024, over a third of cloud security breaches involved valid credentials, while targeted AI-driven deepfake frauds resulted in losses exceeding $25 million. Moreover, phishing attacks leveraging AI generated content saw click-through rates over four times higher than manual methods, demonstrating the threat landscape’s sophistication and scale.

Strategic Investment in AI-Inference Security

To safeguard AI investments, organizations must approach inference security as a strategic priority rather than a reactive measure. Fundamental security practices—such as strict identity management, unified cloud security frameworks, and zero-trust microservice architectures—are essential. C-suite leaders, including CISOs and CFOs, should develop risk-adjusted return on investment (ROI) models that correlate security expenditures with potential breach costs. For example, assessing a 10% probability of a $5 million loss could justify allocating hundreds of thousands of dollars toward preventative measures, ultimately preventing substantial financial