AI data protection: Emerging challenges and solutions in IT for 2026

Artificial intelligence (AI) is rapidly transforming the way IT teams manage operations, automate workflows, and support end users. From AI-powered ticketing to integrated large language models (LLMs) such as Microsoft Copilot, these systems are becoming increasingly embedded in everyday business infrastructure.

However, as AI tools become increasingly powerful and pervasive, they also introduce new challenges to data protection. Sensitive information can be exposed through AI prompts, surfaced in model outputs, or shared with third-party systems that operate outside existing governance frameworks.

For managed service providers (MSPs) and IT teams, this shift necessitates a new approach to data protection that considers the speed, scale, and complexity of modern AI workflows. In this blog, we’ll break down the top challenges in AI data protection and explore practical solutions to help safeguard client environments and maintain regulatory compliance.  

What is AI data protection, and why does it matter now?

AI data protection refers to securing sensitive information used, processed, and generated by artificial intelligence tools. Unlike traditional data protection, which focuses on securing files in storage or transit, AI data protection must address how information flows through models, APIs, and user interactions, such as prompts and other forms of input. This includes:

• Protecting training datasets.
• Limiting data exposure in model outputs.
• Controlling access to AI-enabled systems.

In 2026, these protections are critical. AI tools such as Microsoft Copilot now integrate with Microsoft 365, pulling from SharePoint, OneDrive, Teams, and Outlook to generate content and automate workflows. AI tools can access and surface this data, often bypassing traditional security controls.

AI amplifies the risk of exposure across every data touchpoint. For MSPs and IT teams, securing these AI data pipelines is now essential to prevent breaches, ensure compliance, and maintain client trust.

Download Securing Tomorrow: AI and Data Protection to guide your strategy and help clients and organizations safely adopt Microsoft Copilot and other AI-driven tools.

Top challenges in AI data protection

AI introduces new vulnerabilities that go beyond traditional data security concerns. As models access, process, and generate data across software-as-a-service (SaaS) environments, the risk of exposure, misuse, and compliance failure grows. Key challenges include:

• Shadow AI and unsanctioned tools
  Employees may adopt free AI tools outside of IT governance, but these tools lack proper security configurations and data controls. This is a classic case of “shadow AI” or unauthorized AI tools being introduced into the environment without IT oversight. It creates blind spots and introduces data leakage risks that extend beyond the organization.
• Prompt injection and data exposure
  AI models can be manipulated through malicious prompts to surface sensitive inputs. For example, a bad actor could add a prompt that instructs the model to disregard prior instructions and disclose confidential information. Without prompt controls or output monitoring, models may inadvertently reveal confidential or regulated information.

Learn more about how threat actors are using AI to launch targeted attacks.

• Data leakage through outputs
  Even in sanctioned AI tools, models trained on internal data can reproduce that data in outputs, posing significant privacy, IP, and compliance risks. This highlights the risk of AI systems storing context across sessions or users without strict guardrails in place.
• Model poisoning and adversarial attacks
  Model poisoning happens when attackers intentionally feed inaccurate or misleading data into an AI model during training, causing it to learn incorrect patterns or behave in unexpected ways. For example, a poisoned model might prioritize incorrect alerts or overlook genuine threats. Adversarial attacks involve carefully crafted inputs, such as prompts, images, or files, designed to trick the AI into making an incorrect decision. Both attacks exploit trust in AI systems and can silently undermine operations, compliance, or cybersecurity.
• Third-party and vendor integrations
  AI systems often rely on APIs, plugins, or cloud services that operate outside internal governance frameworks, introducing potential weak points. If a vendor fails to adhere to robust security and compliance practices, sensitive data transmitted through their system may be exposed, mishandled, or stored without consent.
• Data sprawl in SaaS platforms
  As organizations adopt more SaaS tools, especially those with built-in AI, data quickly spreads across multiple platforms, often without centralized visibility or control. This data sprawl makes it harder to track where sensitive information lives, who has access, and how it’s being used.
• Regulatory noncompliance
  AI tools can process, store, or share sensitive data, often without users realizing where that data is going or how it’s being used. If that data includes personally identifiable information (PII), financial records, or health details, using AI without proper controls can quickly lead to violations of regulations such as HIPAA, GDPR, or CCPA.

Each of these challenges underscores the importance of developing AI-aware security strategies, particularly when managing contemporary IT environments. Learn more about integrating AI tools into business processes with intelligent data protection in our guide, Securing tomorrow: AI and data protection.

Compliance and AI regulation

Regulatory compliance now plays a central role in AI data protection, alongside traditional security concerns. As AI tools gain access to sensitive business data, IT teams and MSPs must ensure compliance with evolving privacy and AI-specific frameworks. Key regulatory drivers include:

• GDPR: Applies to any AI system processing EU citizens’ personal data. Key concerns include data minimization, right to explanation, and algorithmic transparency.
• HIPAA: For healthcare-related AI tools, protected health information (PHI) must be encrypted, access-controlled, and auditable, whether used in training data or model outputs.
• EU AI Act: Introduces a risk-based framework that classifies AI systems by threat level. High-risk systems are subject to strict obligations regarding documentation, testing, and transparency.
• State-level laws (e.g., California, Colorado): Expand consumer rights to know how AI-driven systems handle their data, reinforcing transparency and opt-out controls.

Compliance challenges grow when AI outputs are not logged, decisions are not explainable, or models are trained on uncontrolled data. It’s crucial that IT providers help clients or their organization evaluate AI workflows and align them with both existing and emerging regulations.

Solutions and best practices for AI data protection

Effective AI data protection necessitates a multifaceted approach that integrates technical controls, governance, and user education. IT providers are uniquely positioned to deliver these protections through integrated services. Proven strategies include:

• Data governance for AI workflows: Classify sensitive data used in AI systems. Define which datasets can be used for training, prompts, and model outputs, and apply appropriate retention and access policies.
• Secure prompt handling and access control: Limit who can input prompts into AI systems. Implement role-based access and authentication to prevent misuse or unauthorized data exposure.
• Encryption and tokenization: Apply encryption to AI training datasets and outputs, both at rest and in transit, to ensure data security. Tokenization adds another layer by replacing sensitive inputs with anonymized values.
• Model monitoring and output logging: Monitor AI responses for policy violations, unexpected outputs, or signs of model drift. Log all AI interactions to support auditability and compliance.
• Third-party risk management: Vet AI vendors and APIs for security practices. Prioritize tools that offer transparency, audit logs, and secure-by-design principles.
• SaaS security integration: Use tools to monitor AI access within platforms such as Microsoft 365. Visibility into configuration, data loss prevention, and usage is essential when Copilot and similar tools are in play.

These best practices enable MSPs and IT teams to establish AI-ready environments that prioritize data security, maintain compliance, and foster client trust.

Embracing AI data protection with ConnectWise

AI is reshaping how data moves across Microsoft 365, SaaS environments, and LLM-driven workflows, which increases the urgency for strong, multi-layered protection. MSPs and IT teams now need more than traditional backups. They require solutions that safeguard files, identities, SaaS configurations, and business operations across every AI-enabled process.

ConnectWise data protection solutions bring together a comprehensive set of cloud-first products that help organizations secure the full lifecycle of their information. These solutions support backup, recovery, continuity, and SaaS security, enabling teams to reduce risk and maintain full control as AI adoption accelerates.

With ConnectWise, you’ll gain:

• Cloud-to-cloud backup for Microsoft 365 and Google Workspace
  Automated protection for mail, files, Teams content, and identities to prevent loss caused by accidental deletion, configuration drift, or AI-assisted data exposure.
• Business continuity and disaster recovery (BCDR)
  Fast, reliable recovery options that protect servers, applications, and critical workloads. This gives organizations the ability to restore operations quickly after ransomware, outages, or human error.
• SaaS security and configuration protection
  Visibility into permissions, files, sharing activity, and third-party integrations across SaaS platforms, helping teams control how data flows into AI tools such as Microsoft Copilot and other LLM-driven applications.
• Identity and configuration safeguards
  Protection for users, groups, roles, and policies that ensures continuity when AI tools access sensitive identity data or when configuration changes introduce unexpected risk.

Together, these capabilities give IT service providers a unified strategy for protecting data used, processed, and generated by AI systems. With improved recovery readiness, stronger compliance alignment, and greater visibility across SaaS and identity ecosystems, organizations can adopt AI with greater confidence.

Strengthening AI governance starts with the right data protection foundation. Explore how ConnectWise data protection solutions support secure, responsible AI adoption and help safeguard the information fueling modern AI workflows.