Two announcements from NVIDIA GTC 2026 signal a clear shift in enterprise AI strategy. Mistral’s Forge platform introduces a new model where organisations can build and operate their own AI environments. At the same time, NVIDIA is addressing a long-standing barrier to adoption: securing AI infrastructure.
According to TechCrunch (2026), Mistral Forge enables enterprises to “build your own AI enterprise,” deploying models within controlled environments rather than relying entirely on external platforms (https://techcrunch.com/2026/03/17/mistral-forge-nvidia-gtc-build-your-own-ai-enterprise/). In parallel, NVIDIA is developing a security-focused framework designed to mitigate vulnerabilities in AI systems (https://techcrunch.com/2026/03/16/nvidias-version-of-openclaw-could-solve-its-biggest-problem-security/).
Together, these developments mark a transition from AI consumption to AI ownership.
From Consumption to Control
Enterprise AI has largely been delivered through cloud-based services. While this enabled rapid adoption, it limited control over data, governance, and performance.
Mistral Forge reflects a different approach. Organisations can deploy AI within their own infrastructure, creating sovereign AI environments.
This delivers three key benefits:
- Data control: Sensitive data remains within enterprise boundaries
- Customisation: Models can be tailored to specific business needs
- Performance ownership: Infrastructure design directly influences outcomes
This model is particularly relevant in Europe, where data sovereignty and regulatory compliance are critical.
NVIDIA’s Security Pivot
AI systems introduce new risks, including data leakage, model manipulation, and infrastructure vulnerabilities. Historically, security has lagged behind AI innovation.
NVIDIA’s latest development signals a shift towards embedding security directly into AI platforms. This is essential as AI becomes part of core business operations.
Accenture highlights that organisations must embed responsible AI practices, including trust and governance, into how AI systems are designed and deployed (https://www.accenture.com/us-en/services/data-ai/responsible-ai).
Security is no longer an afterthought. It is a prerequisite for scaling AI.
Security-by-Design in Practice
Security-by-design requires protection across the full AI lifecycle:
- Development: Secure training and validation of models
- Deployment: Strong access controls and network segmentation
- Operation: Continuous monitoring and threat detection
This drives demand for specialised cybersecurity services, including penetration testing, red and purple team testing, IT risk assessments, and phishing resilience.
As AI environments evolve, security must evolve with them.
Impact on Communications and Customer Experience
AI is already transforming unified communications and contact centres through automation, analytics, and real-time assistance.
With sovereign AI, organisations gain greater control. They can train models on proprietary customer data and integrate AI more deeply into their platforms.
However, this increases responsibility. Enterprises must secure integrations, protect data flows, and mitigate human risks such as phishing.
The convergence of AI, communications, and cybersecurity is now operational.
Enterprise Networking and Managed Services
AI performance depends on the underlying network. Private AI environments require high-performance connectivity, secure segmentation, and seamless integration.
At the same time, managing AI infrastructure is complex. Many organisations lack the internal capability to handle optimisation, security monitoring, and lifecycle management.
Managed services play a critical role by enabling secure deployment, ongoing management, and continuous improvement.
Final Perspective
The direction is clear. Enterprises are moving towards controlled, secure AI environments.
Mistral and NVIDIA highlight three priorities:
- Control over AI infrastructure and data
- Embedded security across the AI lifecycle
- Integration with enterprise networking and operations
Organisations that align these elements will be better positioned to scale AI securely and deliver long-term value.
Speak with a Damovo expert to explore how sovereign AI, secure infrastructure, and managed services can align with your organisation’s goals.