Google Strengthens Data Center Footprint with Intersect

Google is set to pay $4.75 billion to buy Intersect, a company that builds both data centers and energy systems.

The move is meant to help grow the number of Google’s data centers and give them more power to run jobs for cloud and AI services.

Boosting Cloud and AI Capacity
The move is meant to help grow the number of Google’s data centers and give them more power to run jobs for cloud and AI services.

Joint Projects and Collaboration
Google already had a small stake in Intersect. Under the deal, Intersect will stay under its own name and keep working with Google’s team.

They will continue building joint projects, including a combined power and data center site in Texas.

Some Business Stays Independent
Parts of Intersect’s business in Texas and California will remain separate and continue working with their current investors.

Meeting High Demand Quickly
Google plans to use Intersect’s team and ongoing projects to bring new power and data capacity online faster, helping meet rising demand for cloud computing and AI.

Strategic Takeaways
Securing both computing space and reliable energy is becoming essential for big tech companies to support growth in AI and cloud services.

CISPE, a European cloud provider association, is taking legal action to overturn the approval of Broadcom’s acquisition of VMware.

Concerns Over Rising Costs
The group argues the merger has led to large price increases for VMware customers. Reports show some members facing cost hikes between 800% and 1500%.

Criticism of Regulators
CISPE says the European Commission did not fully examine the risks of Broadcom using VMware’s market power to raise prices and enforce strict contracts.

They describe the Commission’s approval as lacking oversight.

Legal Process and Expectations
The case was filed with the General Court of the European Union.

While court proceedings can be slow, CISPE hopes for a resolution that could restore previous pricing and contract terms for VMware users.

Implications for the Cloud Market
If successful, this action could reduce costs and increase fairness in the European cloud sector, impacting how large mergers are reviewed and managed in the future.

This video takes a hard look at the messy truth behind agentic AI in the enterprise — and why it feels like someone is lying to you.

On one side, big tech vendors, cloud providers, and global consultancies are screaming that "2025 is the year of the AI agent", boasting about customers "deploying thousands of agents" across customer service, IT operations, and back-office functions with massive efficiency gains.

On the other side, independent analysts and people actually building this stuff say most organizations are still stuck in early pilots, that real value is limited to a narrow set of tightly scoped workflows, and that a lot of what’s sold as "agents" is just old automation with an LLM slapped on the front.

Many companies now run critical systems on cloud platforms.

Customer services, internal tools, analytics, and product operations all rely on the cloud.

This brings efficiency but also creates more risk.

Cloud incidents rose 61% in the past year, and most organizations faced at least one security issue.

Misconfigurations Cause Big Problems
Even skilled teams struggle with cloud misconfigurations.

Settings drift, access rules change, and storage locations move.

As systems grow across multiple clouds and environments, manual checks become nearly impossible.

A single unchecked permission or exposed storage can lead to massive breaches.

Regular reviews, automated checks, and steady monitoring are essential to prevent these risks.

Identity Remains the Weakest Link
Most cloud breaches happen because attackers use stolen credentials.

Using valid access helps them blend in.

Incidents show stolen keys or accounts without multi-factor authentication can affect hundreds of companies and cost millions.

Strong identity governance, limited access, key rotation, and monitoring are critical.

Shadow IT Creates Hidden Risks
Organizations often use thousands of cloud applications, many unknowns to IT teams.

This hidden usage can create real security problems.

Leadership needs visibility and controls to manage these unseen environments.

Maintaining Security While Moving Fast
Cloud adoption continues to accelerate, but speed must not outpace security.

Regular monitoring, strict identity management, and clear visibility into all applications are the main steps to reduce risk.

Leadership must ensure cloud growth is balanced with strong security practices to avoid costly breaches and maintain trust.

Airbus is preparing a tender to hire a European cloud provider that meets strict sovereignty rules.

Focus on Data Security and Compliance
The goal is to ensure sensitive data, especially in defense and aviation, stays within Europe and complies with local laws.

This is part of a wider push for secure and independent cloud services.

Open to Multiple Providers
The tender may involve several companies.

Airbus wants flexibility to select the best options for hosting, security, and operational support.

Encouraging Local Cloud Growth
The initiative highlights the need for European cloud companies to strengthen their infrastructure and compete with global providers.

Airbus is positioning itself to protect data, comply with regulations, and support European cloud capabilities.

The rise of AI in multi-cloud setups has made old security tools ineffective.

Traditional firewalls and monitoring systems cannot handle AI’s dynamic and distributed nature.

Models move across clouds, and data flows unpredictably, creating gaps that attackers can exploit.

Blind Spots in Multi-Cloud AI
Using multiple cloud providers increases resilience but also fragments security.

Each provider has different tools, which rarely work together.

This makes it hard to enforce consistent rules and leaves weak spots where attackers can act.

New AI-Specific Threats
Attackers now target AI systems directly with techniques like prompt injection or data poisoning.

These threats are invisible to conventional tools and require context-aware monitoring that understands AI behavior and outputs.

A System-First Approach
Security should focus on the whole AI system, not just individual models or infrastructure.

This means monitoring the full AI lifecycle—from data ingestion and training to inference and automated actions.

Organizations need to track who influences models, how outputs are validated, and how data moves across environments.

Conclusion

Securing AI in multi-cloud environments requires a complete, integrated approach.

Companies must rethink security as an ongoing process to protect data, models, and automated decisions effectively.