The year 2024 has reshuffled the priorities of CIOs at a speed rarely observed. Between the widespread adoption of generative AI in production, the explosion of attack surfaces, and the reconfiguration of cloud architectures, the technical decisions made this year will commit companies for the coming decade.
Confidential computing: the cloud security building block that roadmaps ignore
The debates around multicloud and perimeter cybersecurity mask a technical blind spot. Deploying generative AI models on sensitive data (health, finance, defense) without encrypting this data during processing is akin to locking the front door while leaving the windows open.
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This is the problem that confidential computing solves. The principle relies on hardware enclaves, TEE (Trusted Execution Environments), which isolate code and data even from the hypervisor or the cloud administrator. The data remains encrypted during computation, not just at rest or in transit.
According to Fortune Business Insights, this segment is among the cloud security markets projected to grow the most by 2034, driven directly by AI use cases and the outsourcing of critical workloads. We observe that major cloud providers are now offering confidential instances compatible with GPUs, which removes the last barrier to deploying language models on regulated data.
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To follow IT topics on Geek Newz, this type of technical subject illustrates well the concrete trade-offs that infrastructure teams must make in 2024.
Cybersecurity in 2024: costs on an exponential trajectory despite operational successes
The year 2024 produces an instructive paradox. The cybersecurity of the Olympic Games held up, hailed as a success of coordination between ANSSI, operators, and private providers. At the same time, the overall volume of cyberattacks has exploded across the rest of the economic fabric.
L’Informaticien has documented this duality in its annual reports: the concentration of resources on a one-off event has highlighted the chronic underinvestment of French SMEs and mid-sized companies in incident detection and response.
Cost trajectory of cybercrime
Projections for the period 2024-2029 describe a sharply rising cost curve related to cybercrime, with a direct impact on IT budgets. This trajectory pushes CIOs to reallocate their software and solution budgets towards behavioral detection, zero trust, and automated response orchestration tools (SOAR).
The link with generative AI is direct: attackers use the same language models as defenders. The automated generation of targeted phishing renders static signature-based filters obsolete. Companies that do not integrate a layer of semantic analysis into their email security stack accumulate a backlog that is difficult to catch up on.
Generative AI in production: from prototype to industrial pipeline
The market has crossed a qualitative threshold. In 2024, the question is no longer whether generative AI works, but how to integrate it into existing data pipelines without creating unmanageable technical debt.
Three constraints structure enterprise deployments:
- Governance of training data: traceability of datasets, GDPR compliance, bias management. Data teams must document each source before any production deployment.
- Inference cost: running a large-scale language model on business queries generates cloud bills that can spiral quickly without optimization (quantization, distillation, embedding caching).
- Integration with business tools: artificial intelligence applications do not replace existing software; they integrate with it via APIs. The quality of orchestration between the model and the information system determines the actual value extracted.
Market for data science and machine learning platforms
The segment of data science and machine learning platforms continues its rapid structuring. We recommend that CIOs prioritize solutions that natively integrate model versioning, drift detection monitoring, and connectors to major cloud data lakes. An innovation tool that does not integrate into the existing foundation remains an expensive prototype.
Sovereign cloud and edge computing: hybrid architectures are becoming essential
The debate on data sovereignty has produced concrete effects in 2024. Several sovereign cloud initiatives have reached the production stage, driven by European regulatory requirements and demand from public and banking sectors.
At the same time, edge computing is absorbing an increasing share of real-time computing workloads. Industrial IoT, connected vehicles, point-of-sale video analytics: these use cases require latency that is incompatible with a round trip to a centralized data center.
The target architecture that is emerging combines three levels:
- A public cloud for elastic workloads and model training.
- A sovereign or private cloud for regulated data and critical applications.
- Edge nodes for local processing, pre-inference, and IoT data collection.
This stratification requires infrastructure teams to master multicloud orchestration (Kubernetes, service mesh) and rethink their network security policies. The classic network perimeter no longer exists when data flows between three layers of infrastructure.
Digital professions reflect this evolution: the profiles sought in 2024 combine cloud, cybersecurity, and data engineering skills, three areas that still operated in silos a few years ago. Companies that still compartmentalize these functions into separate teams lose responsiveness to incidents as well as to innovation opportunities.