The AI Boom Is a Chance for Infrastructure Players to Level Up

Summary:

With the unprecedented demand for AI services, infrastructure and data center providers are finding themselves at a critical inflection point. The surge in AI workloads is challenging their ability to scale effectively, maintain high service quality, and uphold security and regulatory compliance programs. Meeting these demands is essential for sustained growth and competitiveness in the AI era. Here we explore how GRC programs should be situated to successfully approach and market services to AI platform and model providers.

Total Addressable Market

Conversations about AI are conversations about infrastructure. Given enough time, they shift gears to power grids and real estate. And for good reason. Models are getting bigger, workloads heavier, and the infrastructure beneath them has been straining to keep up. 

As the AI boom drives datacenter capacity, cost, and compliance to unprecedented levels, providers capable of secure scaling are positioned to gain significant advantages.

How significant are these advantages?

According to Goldman Sachs Research, the largest US hyperscale enterprises are expected to collectively allocate $736 billion in capital expenditures over 2025 and 2026. McKinsey anticipates AI demand will drive an annual increase between 19% and 22% in AI-ready datacenter capacity between 2025 and 2030, with AI accounting for about 70% of total usage by the end of the decade. The supercharged demand is a result of both commercial appetite for AI services that show great potential to offset human capital needs, as well as the investments by the Federal government of the United States and Department of Defense within this area to set the pace in the AI arms race. The question is, can infrastructure providers keep up? And as they expand, can they keep their systems secure?

Organizations that can scale quickly while staying compliant are bound to win the long game, while the rest may end up overwhelmed by excessive workloads and stress.

Co-location is Key

Over a decade ago, as public cloud infrastructure services led datacenter growth, hyperscalers adopted a mixed strategy: constructing their own datacenters while leasing space to accelerate deployment and manage costs.

Today, the race for AI-ready infrastructure is moving so fast that companies are signing workload contracts before they have the necessary infrastructure acquired and in place. As a result, this approach has led to exponential third party or outsourced infrastructure “leasing” capacity from GPU-driven compute environments optimized for AI workloads, rather than procuring and hosting their own infrastructure. 

That rapid need for ready-to-go infrastructure has created an opportunity for independent providers to step in and participate in the AI market and elevate themselves into a position of long-term growth.

Security and Compliance Decide Who Wins 

While increased capacity is a major factor in the current AI landscape, credibility remains equally important. It is widely accepted that AI systems depend on vast datasets, which in turn necessitate sophisticated security measures. Regulatory authorities are scrutinizing industry practices. Providers that demonstrate strong governance and effectively safeguard sensitive workloads will maintain long-term client relationships long after the initial excitement around AI fades.

How do they prove this? 

There’s still no universally acknowledged compliance scheme or certification marker for AI. In the absence of such requirements, leading AI and machine learning (ML) providers are still relying on established systems for Information Security, Privacy, Quality, and Continuity Management Systems. Oftentimes, these are complemented by compliance with HIPAA, PCI DSS, and other industry-specific requirements designed to address industry and marketing needs. Supplying services to local, state, or federal government entities, including the Department of Defense (DoD), represents a strong revenue stream for AI/ML providers. This market brings additional compliance considerations, such as FISMA/FedRAMP, GovRAMP, and DoD CC SRG, which must be incorporated into providers’ compliance planning to ensure they meet the rigor and intent expected by government clients. Specific to protecting proprietary model secrets and factoring, the RAND Corporation’s Playbook for Securing AI Model weights makes the case for tiered security level targets that protect intellectual property, or the crown jewels of the AI/ML model providers.

AI companies rely on their suppliers to uphold the same standards to prevent risks throughout the supply chain. This means compliance is a shared responsibility. Vendors aiming to collaborate with leading AI firms must be prepared to meet an extensive set of contractual security requirements, such as:

• ISO/IEC 27001:2022 (Information Security)
• ISO 9001:2015 (Quality)
• ISO 22301:2019 (Business Continuity)
• SOC 2 (Security, Availability, Confidentiality)
• HIPAA/HITRUST (Healthcare/PHI specific)
• NIST 800-53/NIST 800-171 control baselines
• PCI DSS 4.0 (Card processing standard)

Where Coalfire Helps 

The AI services boom presents significant growth opportunities, and a structured GRC program makes the difference. 

Coalfire is the premier security compliance advisory services provider with a large stable of SMEs across commercial and regulatory frameworks, including the frameworks listed above. Based on our compliance expertise, industry knowledge and experience, Coalfire has established a quick start methodology to standup a single comprehensive GRC program that can address all requirements typical for both AI infrastructure providers and datacenter providers, regardless of number of sites/locations. The methodology is curated to assist organizations at any stage of the journey - from a position of having secured land to build a datacenter, to a finished platform that needs a final independent review– Coalfire can be that partner that will ensure your preparedness for certification or attestation.

Coalfire’s quick-start model is one example - it brings every major framework into one manageable system, covering everything from gap assessments to audit prep and ongoing maintenance:

• Rapid gap and remediation workflow to design compliance on the fly
• Streamlined documentation and integrated management systems
• Fully in-house, credentialed audit and advisory team with flexible scheduling
• Dedicated project team and project management resources
• Technology-agnostic work products and deliverables
• GRC-as-a-Service to provide ongoing support for continuous improvement

Coalfire handles the bulk of compliance-related tasks, significantly reducing the workload and responsibilities typically placed on internal teams. By leveraging Coalfire’s quick-start, modularized methodology, organizations can expect to be well-prepared for certification and attestation audits within a six to eight month timeframe.

This approach transforms compliance into a continually renewable asset, helping datacenter providers to meet AI-era demands while remaining equipped for future requirements and industry changes.

Key Takeaway

The AI boom presents significant growth opportunities among infrastructure providers. Those who develop strong capabilities and credibility will lead the industry over the next decade. Taking an all-inclusive and flexible approach to security and compliance enables organizations to become reliable partners in an AI-focused environment, positioning themselves for new business models that handle sensitive data in sectors like finance, healthcare, and regulatory services. 

Coalfire stands ready to guide you through both compliance and business case decisions related to your AI infrastructure. Contact us to find out more.