Agentic AI is quickly becoming the defining trend of 2025.
Gartner predicts that by the end of this year, 33% of enterprise software applications will include agentic AI capabilities. This means that, whether leaders realise it or not, agentic AI is becoming a default feature inside the systems they already run their businesses on.
For instance, Microsoft has expanded its suite of productivity tools with the integration of Co-Pilot, its advanced AI assistant designed to streamline workflows across Microsoft 365, Teams, and Dynamics.
While this update promises businesses huge productivity and efficiency gains, it also creates significant data security challenges. In single-cloud environments these challenges are complex. In multi-cloud environments, where providers each have their own access controls, standards, and models, that complexity multiplies. The result is blurred lines of responsibility and hidden exposures attackers can exploit.
In this article, we’ll explore the security challenges of multi-cloud AI deployments and how you can protect your business.
Security Risks in Multi-Cloud AI Deployments
In this section, we will look at the following risks in muti-cloud AI deployments:
Ambiguity in the AI Shared Responsibility Model
The shared responsibility model is a framework that distinguishes between the security and compliance responsibilities of cloud service providers (CSPs) and their customers. In this case, the integration of Co-Pilot across multiple cloud environments creates complexities, as organizations may face challenges in determining who is responsible for securing AI training data, models, and outputs at each stage of the AI workflow. When these obligations are unclear, security gaps can emerge, increasing the risk of data breaches and unauthorized access.
With single data breaches averaging US$4.44 million globally in 2025, down from US$4.88 million in 2024, and in Canada soaring to CA$6.98 million per incident, clarity over responsibility and strong security measures aren’t optional. These figures underscore the critical need for organizations to establish clear contracts and service level agreements (SLAs) that outline not only their internal duties but also the expectations of their cloud service providers. This is highlighted in IBM’s Cost of a Data Breach Report and discussed in MSP Corp’s “4 Critical Cybersecurity Trends for 2025”
AI-Specific Access Control Complexities
Managing access control for enterprise AI involves setting granular permissions to determine which personnel can train models, view AI insights, or modify AI configurations. However, this complexity may be heightened when organizations deal with multi-cloud environments due to the varying access control mechanisms in each platform. In addition to data leaks, potential misuse of AI capabilities can also arise. To ensure consistent and secure access control, organizations must implement robust identity and access management (IAM) frameworks that span all cloud environments. Reducing data breaches and compromised AI models also entail regularly reviewing and updating access permissions and employing automated tools to monitor and enforce access policies.
Data Flow Risks Across Environments
When data moves across different cloud environments, this can introduce specific risks, such as data interception, loss, or corruption. These risks are exacerbated by the varying security protocols and standards across platforms. In the context of AI, the complexity increases as AI models require large datasets that are transferred and processed across multiple environments. This data sprawl can lead to vulnerabilities such as data leaks, where sensitive information might be exposed during transfer. Additionally, AI models themselves can be targeted for data poisoning attacks, where malicious actors manipulate the data to skew the model’s outputs. These risks underscore the importance of a proactive approach to data protection, where data security posture management (DSPM) identifies vulnerabilities across cloud environments and Guardian Shield leverages AI to detect and neutralize emerging threats, helping organizations safeguard sensitive information against increasingly sophisticated adversaries. You can learn more about how Guardian Shield works here.
DSPM is a cybersecurity practice that identifies sensitive data across cloud environments, assesses vulnerabilities, and automates compliance monitoring. DSPM provides organizations with visibility over where sensitive data resides, how it moves, who accesses it, and what security risks exist within their data infrastructure. By implementing DSPM, organizations gain a comprehensive understanding of their security posture, allowing them to pre-emptively address vulnerabilities and quickly resolve data security issues before exploitation can occur. While data protection and backup remain critical to restoring data integrity, organizations without DSPM will struggle to control what individuals can put in their environments, including content designed to poison their data. It is also equally important that organizations control who can download, delete, modify, as well as add, create, or upload data.
By leveraging a proactive stance in security, organizations can ensure data integrity and security across diverse environments — critical areas to maintain the reliability and trustworthiness of AI-driven processes. AI systems are non-negotiable in today’s workforce as they open the doors to unparalleled productivity gains and informed decision-making. These security considerations, rather than roadblocks, are prime opportunities for organizations to enhance their data management before they invest in their AI system of choice.
Strategic Protection for Leveraging AI
In this section, we will look at the following security guardrails that organizations must implement. This includes:
Cloud Security Posture Management (CSPM)
While DSPM focuses on identifying and protecting sensitive data across cloud environments, CSPM is geared toward maintaining the overall security posture of cloud infrastructures by continuously assessing and remediating risks. CSPM tools are essential for identifying and remediating risks across cloud infrastructures, including infrastructure-as-a-service (IaaS), software-as-a-service (SaaS), and platform-as-a-service (PaaS). These tools continuously assess the security posture of cloud environments by maintaining an up-to-date inventory of assets and configurations. CSPM solutions provide detailed visibility into the security state of resources and offer guidance for fortifying an organization’s security posture. These solutions detect misconfigurations, compliance violations, and potential vulnerabilities, ensuring that security policies are consistently enforced across multi-cloud environments. By automating the assessment and remediation processes, CSPM tools reduce the risk of human error and enhance overall cloud security.
Unified Identity and Access Management (IAM) Frameworks
Unified IAM frameworks are critical for managing user identities and access permissions across all cloud environments. These frameworks integrate various identity management components, such as access management (AM), privileged access management (PAM), and identity governance and administration (IGA) into a cohesive system. By providing a centralized platform for identity management, IAM frameworks ensure that the right users have the right access to resources while preventing unauthorized access. They support single sign-on (SSO), MFA, and role-based access control (RBAC), enhancing security and user experience. Unified IAM frameworks also streamline compliance efforts by providing comprehensive audit trails and access reviews. You can refer to this article for more insights: Authentication vs Authorization: Enterprise Security Guide
Automated Security Monitoring and Incident Response
When security monitoring and incident response are automated, they deliver continuous visibility and rapid response capabilities across the entire multi-cloud ecosystem. These tools use advanced algorithms and machine learning to detect anomalies, potential threats, and security incidents in real-time.
By automating repetitive tasks, such as log analysis and threat detection, these solutions enable security teams to focus on more strategic activities. Automated incident response platforms can quickly contain threats and mitigate risks, reducing the impact of security breaches. They also provide centralized dashboards for reporting and managing security alerts, ensuring that organizations can maintain a robust security posture and respond effectively to evolving threats.
Data Loss Prevention (DLP) Solutions
DLP solutions are designed to protect sensitive data from unauthorized access, misuse, or exfiltration. These solutions enforce security policies based on the content and context of data, whether it is in use, in motion, or at rest. DLP tools monitor data flows across the organization and apply consistent policies regardless of where the data resides (such as on-premises or in the cloud). By detecting and blocking unauthorized data transfers, ensuring compliance with regulatory requirements, and protecting intellectual property, these tools help safeguard organizations from data breaches. DLP solutions also provide visibility into data usage patterns, enabling organizations to identify and mitigate potential insider threats and data loss incidents.
To enhance resilience, organizations can integrate DLP with robust backup and disaster recovery systems. These systems ensure data integrity by providing automated backups, granular restores, and allowing for swift recovery in case of data loss or corruption.
By leveraging a proactive stance in security, organizations can ensure data integrity and security across diverse environments — critical areas to maintain the reliability and trustworthiness of AI-driven processes. AI systems are non-negotiable in today’s workforce as they open the doors to unparalleled productivity gains and informed decision-making. These security considerations, rather than roadblocks, are prime opportunities for organizations to enhance their data management before they invest in their AI system of choice.
Building a Resilient AI Foundation
The integration of Co-Pilot across Microsoft 365 demonstrates the transformative potential of AI in enhancing organizational productivity and operational efficiency. However, as businesses embrace these technological advancements, addressing security challenges in multi-cloud environments becomes critical. A holistic security solution is essential, encompassing advanced encryption, real-time monitoring, and clear responsibility delineation between internal teams and CSPs. By combining productivity gains from Microsoft Co-Pilot with advance defenses like Guardian Shield, organizations can confidently navigate the complex landscape of AI technologies.
Conclusion
AI adoption is accelerating across Canada. For SMBs, it offers the potential to cut costs, improve decision-making, and deliver better experiences to customers. Yet these benefits mean little without security to match.
The choice is straightforward: AI can either strengthen an organization or expose it. Success depends on whether the foundation is resilient, secure, and well-governed.
MSP Corp helps Canadian businesses build that foundation by combining Microsoft Co-Pilot for productivity with Guardian Shield for AI-driven security. To learn more about building AI securely in a multi-cloud environment, contact our team today at cybersecurity@mspcorp.ca