The analyzed threat concerns the operational limitations of pay-per-use AI security agents deployed in blue-team cybersecurity scenarios, such as log triage, recursive investigation, correlation, and incident reconstruction. During testing, it was observed that deep reasoning tasks performed by these AI agents trigger non-linear spikes in token consumption, which are not proportional to the complexity or length of the queries. This behavior results in unexpectedly high usage costs and can cause workflow interruptions due to metered billing constraints imposed by competitors' AI service models. The case study shared highlights that pay-per-use models struggle to handle the computational load required for continuous, iterative analysis under pressure, which is typical in real incident response scenarios. Consequently, these billing models either slow down the defensive operations or become prohibitively expensive, impacting the efficiency and effectiveness of cybersecurity teams. The report advocates for unlimited usage AI models as a more suitable alternative for continuous defensive operations, as they avoid the token spike problem and enable uninterrupted workflows. While this issue does not represent a direct security vulnerability or breach, it poses a significant operational threat by limiting the practical usability of AI agents in critical security functions. No specific software versions or exploits are identified, and the source is a Reddit NetSec discussion with minimal technical indicators. The severity is assessed as medium due to the impact on availability and operational continuity rather than direct compromise of confidentiality or integrity.

For European organizations, this threat primarily affects the operational efficiency and cost-effectiveness of AI-driven cybersecurity defenses. Organizations relying on pay-per-use AI security agents may experience workflow interruptions during incident response due to token consumption spikes, leading to delays in threat detection and mitigation. The increased costs associated with non-linear token usage can strain cybersecurity budgets, potentially limiting the extent to which AI tools are employed. This is particularly critical for organizations with high incident volumes or complex investigations requiring iterative AI analysis. The impact on availability of AI services during critical moments can reduce the overall resilience of security operations centers (SOCs). Additionally, smaller organizations or public sector entities with constrained budgets may find pay-per-use models financially unsustainable. The threat does not directly compromise data confidentiality or integrity but can indirectly increase risk exposure by hampering timely incident response. European entities with advanced AI adoption in cybersecurity, such as financial institutions, critical infrastructure operators, and government agencies, are most susceptible to these operational challenges.

1. Transition to AI security agents offering unlimited usage or flat-rate billing models to avoid disruptions caused by token consumption spikes. 2. Optimize AI query design by breaking down complex reasoning tasks into smaller, more efficient queries to reduce token usage. 3. Implement real-time monitoring and alerting for AI token consumption to detect and manage unexpected spikes proactively. 4. Combine AI analysis with traditional automated tools to reduce reliance on deep reasoning AI tasks during high-pressure incidents. 5. Engage with AI service providers to understand token consumption patterns and negotiate enterprise agreements tailored for continuous security operations. 6. Train SOC analysts on cost-effective AI usage practices and incorporate AI usage budgeting into incident response planning. 7. Evaluate alternative AI platforms with more predictable billing and performance characteristics before deployment. 8. Develop fallback manual procedures to maintain incident response continuity if AI services become cost-prohibitive or unavailable.