AI Resilience: What It Is and Why It Matters More Than Ever

AI is now critical in mission-driven applications, making resilient AI infrastructure essential.

‍

Over 80 percent of businesses have adopted AI to some degree and more are planning on doing so as the tech becomes increasingly accessible, sophisticated and embraced by businesses and organisations. These AI systems require stable compute power and robust data pipelines for both training and inference.

‍

However, as regulations evolve, resilience is shifting from a technical best practice to a compliance mandate.

‍

In their AI Predictions 2025, Omniscien noted: “Compliance is now a critical foundation for sustainable growth in an increasingly regulated and data-driven world. The rise of generative AI tools has compounded the risks, especially around data privacy and sovereignty. Businesses that previously overlooked these risks are now finding them impossible to ignore.”

‍

AI Infrastructure Demands | Training vs. Inference

‍

‍

Training AI models such as ​​GPT are compute-intensive and data-heavy, demanding scalable compute and large storage capacity. While there are clear benefits to working with them and the opportunities to enhance how organisations operate, they simultaneously generate risks too.

‍

Key Risks:

‍

o Hardware failures and power outages

o Cloud dependency and unpredictable costs

o Data privacy and regulatory constraints

‍

Regulatory Impact

‍

There is also a regulatory impact as all AI training must comply with GDPR and any data localization legislation.

‍

Social media platform X, previously known as Twitter, paused training its AI chatbot Grok because of concerns raised by regulators regarding the use of European Union user data for AI training. They claimed that this would potentially violate data privacy laws like GDPR. These concerns meant X could no longer use EU user data to train their AI system due to concerns about how this personal information was being handled.

‍

X is not the only social media platform to fall foul of this. In September 2024, LinkedIn suspended the use of UK user data after concerns were raised by the Information Commissioner’s Office (ICO) about user privacy. Social media giant Meta faced a similar regulatory hurdle back in June 2024 too.

‍

Inference: Real-Time AI with Availability Needs

‍

‍

AI inference runs at the edge, in the cloud, or hybrid environments, requiring low latency and reliability. Without this in place, there are risks to workflows and productivity, showing how robust AI infrastructure is essential.

‍

Key Risks:

‍

o Compute availability bottlenecks

o Failover challenges in real-time applications

o Latency affecting mission-critical tasks

‍

Regulatory Focus:

‍

Increasingly, governments are beginning to mandate resilience for AI inference with action plans being formed around the world. The UK launched its own strategy around keeping up with AI innovation in January 2025.

‍

Key Threats to AI Infrastructure Resilience

‍

1. Compliance & Data Sovereignty

‍

AI models must comply with data localization laws (EU AI Act, GDPR, U.S.

regulations) and need geographically redundant AI infrastructure.

‍

2. Cloud Dependency & Centralization Risks

‍

AI infrastructure relies heavily on AWS, Azure, and Google Cloud, creating potential single points of failure. Regulators may push for multi-cloud and decentralized compute solutions.

‍

3. Energy & Sustainability Challenges

‍

AI training is energy-intensive. For example, training a large language model like GPT-3, for example, is estimated to use just under 1,300 megawatt hours (MWh) of electricity; about as much power as consumed annually by 130 US homes. Therefore, future regulations may cap workloads. Compliance will require renewable-powered AI compute and dynamic workload balancing.

‍

4. Security & Cyber Resilience

‍

AI faces model poisoning, adversarial attacks, and ransomware risks. Prompt injection is seen as one of the most damaging - and this is where malicious actors can manipulate an LLM's input prompts to generate harmful or inappropriate outputs. Future AI regulations may mandate cyber resilience and attack mitigation.

‍

Strategies for AI Resilience

‍

‍

At Redsand, our team is working tirelessly to evolve solutions capable of responding to the developments in this landscape. Some include strategies for enhance resilience include:

‍

✅ Hybrid & Multi-Cloud Computing – Reduces cloud provider dependency.

✅ Edge Computing for Inference – Lowers latency and improves availability.

✅ Decentralized AI Compute – Supports sovereign and localized AI infrastructure.

✅ Energy-Efficient AI Workflows – Helps meet regulatory power usage limits.

✅ Security-First AI Deployment – Embeds resilience at the infrastructure level.

‍

Regulatory Outlook: What’s Coming?

‍

Different regulations and pieces of legislation are coming into effect in regions around the world to improve infrastructure and resilience - and this will lead to new challenges for stakeholders. For example, the EU’s Digital Operational Resilience Act (DORA), approved in late 2022, requires financial institutions to strengthen resiliency by taking measures to mitigate cyber-attacks and ensure uptime.

‍

China announced a plan to decrease the average power usage effectiveness (PUE) of its data centers to less than 1.5 by 2025. Here are some potential impacts of this regulatory landscape

‍

�� Mandatory AI uptime & failover standards

�� Audit trails for AI decisions

�� Stricter data sovereignty laws

�� AI energy consumption regulations

‍

Conclusion: Preparing for AI Compliance

‍

Resilience is no longer just about uptime - it’s about compliance, security, and sustainability. AI providers must adapt now to meet future regulatory requirements. Investing in redundancy, decentralization, and energy-efficient AI infrastructure will define the next era of AI development.

‍

Want to find out how Redsand can support you on your AI journey?

‍

Contact our team now for more.

‍