Microsoft Ignite: Securing AI Applications with Managed Services

What’s keeping most IT leaders up at night? It’s likely artificial intelligence. The power of OpenAI, Copilot and large language models is exponential. And it’s both exciting and nerve-racking to unlock the technology’s full potential.

Microsoft CEO Satya Nadella described AI as a “new neural algebra to help solve complex problems. We can detect patterns involving people, places and things.” 

But with that long-term memory and rich data comes immense responsibility. Train a model well, and you unlock an agentic world of productivity. Train it wrong, and a company could inadvertently release private information and create vulnerabilities.

That’s why experts at this year’s Microsoft Ignite, hosted online and in person in Chicago, said businesses should prioritize security above all else. And though some generative AI tools have security built in, managed services are a smart way to secure your tech stack.

In fact, the global managed services market is expected to reach more than $393 billion in 2025, according to Straits Research. Managed services teams at IBM also found that using AI capabilities to automate 70% of alerts accelerated their threat management timeline by more than 50% within the first year of use, as noted by IBM in the MIT Technology Review.

Here are a few ways that businesses can use such managed services to their advantage.

The complexity of AI models and the sheer volume of sensitive data they handle makes them a lucrative target for cybercriminals. But securing the AI application layer while protecting the underlying infrastructure and data is a tough task.

For starters, teams must consider robust security measures that will run safely in the cloud to prevent unauthorized access and data breaches. IT leaders must also protect the integrity of AI models from tampering or communicating with adversarial attacks, as doing so would produce untrustworthy results. And then there are regulatory compliance requirements to consider.

Given these complexities, managing AI security internally can be overwhelming for many in-house IT teams. That’s where managed services can provide scalable, expert-driven solutions.

“The benefit of CDW and our partner ecosystem is that we can help IT leaders as they integrate with AI,” said Al MacKinnon, solution architect and team lead within CDW’s digital experience practice. Whether your team is getting Microsoft Copilot up and running or sorting data with generative AI, these experts can view the entire initiative through a security lens, he said.

Here are some services that help secure AI applications and workflows:

• Security Assessments and Gap Analysis: CDW conducts comprehensive assessments to identify vulnerabilities in AI environments. By understanding an organization’s unique requirements, CDW can develop tailored security strategies.
• Proactive Threat Monitoring: With CDW’s managed security services, organizations benefit from 24/7 monitoring and threat detection. This proactive approach ensures threats are addressed before they impact AI applications.
• Incident Response and Recovery: In the event of a security breach, CDW’s incident response team steps in to mitigate damage and restore normal operations quickly. The team’s expertise in AI applications ensures a targeted response.
• Ongoing Optimization: CDW helps organizations continually refine their AI security posture through regular audits and updates, ensuring that both the application and its underlying infrastructure remain resilient against emerging threats.

Ultimately, a managed service provider helps customers harness AI in everyday workflows and helps organizations get the most value from those solutions, explained Nathan Cartwright, chief AI architect CDW.

IT leaders can also take advantage of Microsoft’s security ecosystem, which is built to address the end-to-end needs of AI application security.  Here’s how their solutions contribute to a secure AI environment:

• Microsoft Azure Security Center: Azure’s Security Center offers advanced threat protection for workloads running in Azure, on-premises or in hybrid environments. This platform continuously assesses security risks, identifies vulnerabilities in AI workloads and helps mitigate threats.
• Azure AI and Machine Learning Security: Microsoft provides a secure foundation for AI development with tools such as Azure Machine Learning. Features such as identity management, role-based access controls and data encryption ensure that AI models are developed and deployed securely.
• Microsoft Defender for Cloud: A critical component of AI security, Microsoft Defender provides real-time protection against attacks targeting cloud-based AI applications.
• Compliance with Regulatory Standards: Microsoft’s compliance tools make it easier for businesses to follow the General Data Protection Regulation, HIPAA, the Payment Card Industry Data Security Standard, and ISO 27001 requirements. This is particularly important for organizations deploying AI applications in regulated industries, such as healthcare and finance.

“AI can improve productivity. It can improve customer service. It can save businesses money. But which one of those are your goals?” MacKinnon said. Say for example, the goal is cost-efficiency and reducing downtime. IT leaders can seamlessly integrate CDW and Microsoft’s managed services simultaneously. The two are designed to coexist, ensuring a cohesive and effective security ecosystem.

In one example shared during Microsoft Ignite, a manufacturing company deployed AI-powered predictive maintenance solutions to optimize operations across multiple facilities. The AI system collected real-time sensor data from machinery to predict equipment failures, reducing downtime and saving costs.

Using a combination of Microsoft Azure Security Center and CDW’s 24/7 threat monitoring and compliance management security services, the manufacturer was able to achieve its goal and secure data at scale across locations,  all without impacting the AI model.

The takeaway? These services allow organizations, regardless of their size or complexity, to secure AI applications.