Industry spending on implementing artificial intelligence (AI) continues to skyrocket. Bain estimates that: The AI hardware market alone is expected to grow to $1 trillion by 2027 Annual growth rate is 40-55%. Despite these large investments, many organizations still do not see a return on investment (ROI). In fact, a recent study from MIT found that: 95% of organizations have zero ROI From the Generative AI (GenAI) project.
AI clearly shows great potential and offers unparalleled capabilities in large-scale data analysis, automation, and decision-making. Nevertheless, the momentum of AI adoption poses significant security challenges that organizations are only beginning to grasp. These risks often first emerge with a sudden increase in cloud infrastructure costs.
Implementations of artificial intelligence are creating new security loopholes and vulnerabilities that traditional security frameworks were not designed to address. These include adversarial attacks that manipulate AI decision-making, data poisoning that corrupts training datasets, and attacks on machine learning models that exploit weaknesses in algorithms.
AI systems, especially those that use machine learning (ML), analyze large amounts of data to generate predictions and automate decision-making. As ML systems become more deeply integrated into IT infrastructure, their vulnerabilities present new attack opportunities for malicious actors. The complexity of these systems can hide the source of security signals, making it more difficult to identify threats using standard monitoring methods.
The competitive environment has created an “AI must-have” perception, leading organizations to deploy AI projects in an increasingly haphazard manner. In a hurry to catch up with competitors, companies are deploying AI solutions without proper security controls or cost oversight. These rapid and poorly planned deployments create security loopholes that organizations are forced to address later.
Security and FinOps: An unlikely partnership
Thankfully, IT departments have an unexpected ally in identifying AI-related security issues: cost optimization tools. While security flaws remain elusive and difficult to spot, the economic impact of security threats such as resource hijacking, misuse, and system inefficiency is always visible in cloud billing data.
As a result, FinOps and security teams can collaborate to address AI risks. Identity management systems can help teams identify workloads from both perspectives. Security teams can clearly see who is doing what, while FinOps teams can track where funds are being spent. This dual visibility creates a comprehensive view of potential issues.
A recent example shows this principle in action. A company’s IT team noticed significant BigQuery cost overruns for no apparent reason. Further investigation revealed that a security breach was the cause. From a security perspective, this situation could have been avoided if the security controls had been layered during implementation rather than being added as an afterthought. Similarly, if FinOps practices were implemented with the same intent as security measures, cost anomalies would have been discovered sooner.
The need for intentional implementation
Competitive pressure to innovate quickly and achieve market leadership positions is creating a situation where organizations on the verge of reaching the “top right quadrant” are approaching a dangerous threshold where they may fall completely. In the rush to innovate, organizations often bypass critical security and cost controls.
The speed of today’s innovation cycles forces IT teams to make changes without proper visibility or testing. Then, when something breaks, IT loses trust with both customers and internal stakeholders, putting future AI projects at risk.
To avoid this situation, organizations must intentionally pause during AI implementation and adjust their security measures and cost optimization practices. Despite being critical to long-term success, this approach is under-adopted.
The way forward: context awareness
Modern FinOps evolutions are focused on not only increasing visibility into cloud costs, but also increasing situational awareness of those costs. Understanding this context is critical when identifying AI-related security risks, as unusual spending patterns often indicate potential security issues.
The goal is to develop a comprehensive view of infrastructure and spending that AI tools can turn into actionable insights for decision makers. For organizations implementing AI systems, this means establishing FinOps practices that allow costs to be traced back to specific AI workloads and processes. Once an AI system is triggered by a customer interaction, organizations need to be able to trace it back to a reasonable estimate of the cloud cost to complete that transaction.
Building sustainable AI security
Rather than rushing to implement AI solutions, organizations should adopt a crawl-walk-run strategy. This means:
- Start by properly measuring and labeling your AI workloads.
- This can be done via a third party library
- Establish cost standards for AI operations
- Implement a monitoring system that can detect unusual spending patterns
- Create a continuous feedback loop between SecOps and FinOps teams
The most successful organizations will not be those that adopt AI first, but those that adopt it most sustainably. By considering cost optimization tools as security allies and deploying AI systems with proper financial oversight, organizations can identify and manage security risks early and prevent them from escalating into major incidents.
As AI advances beyond the current “illusion of efficiency,” organizations with solid foundational practices will be better equipped to scale their AI initiatives in a secure and cost-effective manner. In the cloud era, it’s important to understand that security and financial stability are becoming more interconnected, and monitoring one can provide valuable insight into the other.
The worst mistake an organization can make is waiting for the perfect tool or complete understanding before starting these practices. Now is the time to start integrating FinOps and AI security practices while building the situational awareness needed to effectively manage both cost and risk.
