business resources
7 Security Risks That Grow Alongside AI and Cloud Adoption
12 May 2026
Artificial Intelligence (AI) and Cloud technology were once considered separate digital transformation initiatives. However, as more organizations adopt cloud-based technology to automate, personalize, and predict customer interactions, the threat landscape is shifting in new and unexpected ways.
AI + Cloud = Scale.
Operating with AI enables a new way to operate, with new insights and new opportunities to automate. The Cloud delivers scale fast without big up-front investment.
Organizations may be deriving benefits from adopting AI and Cloud Computing, but they are likely to face new risks from integrating these technologies. Managing these risks requires organizations to have a more complete view of their expanded attack surface, manage Identity and Access from end to end, and ensure appropriate monitoring and control of data movement across their distributed environments.
Security can no longer be an add-on; it has to become part of an organization’s overall culture and mindset. As AI and cloud continue their rapid transformation of the digital landscape, understanding the emerging trends and risks within these areas is critical for enterprise executives, their IT teams, and security professionals alike.
1. Misconfiguration Across Expanding Cloud Environments
Cloud misconfiguration is currently one of the most dangerous cybersecurity risks facing organizations today. With the rapid pace of evolution in the cloud ecosystem, teams and stakeholders are making configuration changes to their cloud environments daily. The rapid growth in the number of objects, such as buckets, permissions, API endpoints, workloads, and virtual networks, in these environments raises the risk of data loss exposure or accidentally making sensitive services public.
As an organization grows, it is not uncommon to discover that it is split across multiple clouds in different regions and departments. Monitoring and maintaining security posture can be a daily struggle for security teams. A regular cloud security audit can play a large role in an organization’s ability to manage risk. The audit process helps organizations to identify areas of drift, overexposure, and unknown policy gaps before they become security incidents.
Cloud and other dynamic environments present new risks; the greatest risk is not a single misconfigured setting. The greatest risk to organizations is the cumulative effect of a large number of small, often inadvertent, misconfigured settings that have gone undetected until discovered by an attacker (or, indeed, another auditor).
2. Overexposed Identities and Permissions
As companies move to the cloud and use AI tools, the challenges of managing identity grow more complex. Identity is tied to the access employees, contractors, service accounts, integrations, and AI agents need to systems. Over time, permissions on these identities often grow as well. All this leads to more identities with greater access than they actually need.
This makes identity compromise particularly dangerous because attackers can use it to enable lateral movement within cloud storage environments. An individual compromised credential, an automated API key, or a service account with too many permissions may grant access to sensitive assets while avoiding obvious detection.
The use of AI adds another layer of complexity to this issue. Many AI workflows require automated access to data, models, and cloud resources. If the identities used to access these systems are not actively governed, the organization will ultimately build a security posture that is fast but fragile. A least-privilege approach is no longer a theoretical best practice. It is one of the few methods available to significantly limit the blast radius when something goes wrong.
3. Sensitive Data Exposure Through AI Workflows
The effectiveness of AI systems depends heavily on the quality of their data sources. This introduces a conflict between the demand for innovation and the need to protect confidentiality. It has become so common for businesses to request AI tools to aggregate customer behavior, assist with analyst summaries, and provide operational insights. The reality is that each time an AI model ingests a data supply, the company is making a decision to expose that data.
This problem becomes even more pronounced when employees use applications or systems that have not been sanctioned by the organization to perform their work, or when AI awareness is built into the connection from the cloud to the AI model's source or destination without proper classification or access rules. All sensitive information, including personal data, financial data, source code, and confidential business information, is at risk of being exposed to unintended recipients.
While this is one aspect, the additional aspects include AI data model training and prompt input logging, as well as processing completed or output by third parties. When data is consumed by an AI process, an organization needs to track where it goes, who is entitled to view it, and how long it is retained or reused. If organizations do not understand where AI data originated or where it resides after it is created, they can create compliance violations long before generating measurable business value.
4. Shadow AI and Unmanaged Tool Adoption
Shadow AI is becoming a larger risk for many companies today. Shadow AI is the use of AI capabilities such as tools, models, and plug-ins without approval or governance. These practices are often undertaken with the very best of intentions, as teams simply want to work faster or find an alternative way to solve a problem. However, if those tools connect to company data and/or cloud environments without oversight, it is highly likely that substantial risk will be incurred very rapidly.
The danger of Shadow AI stems primarily from the fact that most of these tools and plug-ins do not appear in most companies’ central security functions. For example, a company may believe it has only one approved AI platform. However, multiple teams may also be using a wide range of tools, including browser extensions, copy-paste workflows, and third-party services that have not been reviewed for data access or handling.
Due to these facts, AI governance cannot simply be a policy document. AI governance must be discoverable, trackable, and have clear usage rules for AI capabilities and the ways they are used. Companies that are working to mature their processes are well served by developing a structured multicloud strategy to help ensure AI adoption occurs within an appropriate control environment rather than outside it.
5. Vulnerabilities in Cloud-Native Apps
When using cloud computing, companies can release their software more quickly than before. They also use components such as APIs, microservices, and infrastructure-as-code to achieve this. While being able to produce more products and services is beneficial to innovation, it also allows for programming errors (i.e., vulnerabilities) to be found in the production environment sooner than ever.
In real-world terms, vulnerabilities found during the development process can now occur within the code repository that contains non-encrypted passwords, the code contained within the cloud APIs created, outdated libraries used within the cloud service, configuration issues with the cloud service containers, and weak security enforcement around the cloud-native services being run in the cloud. When you add AI to your cloud-native services, it opens up even more attack surfaces, since the AI services depend on additional packages, endpoints for ML model execution, and automated data pipelines.
Each of these vulnerabilities puts the environment as a whole at risk since each service is highly dependent on other services and data. Therefore, one service can create a vulnerability in another service, because a weakness within a single service can expose it to another service if the two share permissions, secrets, or service accounts. Therefore, security teams must consider the relationships between services when securing an environment, rather than focusing solely on asset isolation.
6. Third-Party and Supply Chain Dependencies
Using both cloud computing and AI depends on third-party providers. While cloud providers, SaaS providers, APIs, model providers, open source packages, and external integrations can add new capabilities, they also increase business risk.
When a business starts relying on external services, it gets harder to track where data goes and what controls are in place to keep it secure. Sometimes, just one compromised, unsecured, or misconfigured component can put several systems at risk.
AI adds even more risk as model ecosystems keep changing. When organizations connect to LLM APIs, plugins, and open source AI components, they might not know how those AI tools are maintained or secured. This leads to several layers of application dependencies that are tough to monitor unless supply chain security is built into the core cloud security program.
7. Compliance Drift in Fast-Moving Environments
Perhaps the most difficult threat to detect is compliance drift. When an organization adopts cloud technology, many new products and services become available. Therefore, the organization may have started its journey with a solid foundation in compliance; however, with new workloads added, the environment no longer meets the original control and standard requirements.
AI also increases the speed at which an organization can drift from compliance because new AI-based workflows are typically implemented faster than governance frameworks can adapt. Therefore, when implementing AI, organizations may need to update data retention practices, access review cycles, customer consent obligations, and audit log requirements. If changes are not made, the organization risks a disparity between what it has put in place to ensure compliance and what is actually occurring within it.
This is why compliance should be treated as an operational outcome and not just another project. Companies that establish continuous visibility into their cloud workloads and the use of AI technologies are much better positioned to comply with legal and contractual obligations as they continue to develop these solutions.
Building a More Resilient Security Model
Cloud computing and AI don't create entirely new risk factors. Instead, they accelerate existing risks and weaknesses, and introduce new ones that grow faster than most security systems can handle.
That doesn't mean you should slow down your innovation. Instead, as you keep moving forward, you'll need to build security into every part of your work. Ensure you maintain ongoing visibility into your cloud environments. Apply governance to your AI workflows. Tighten access privileges more than before. Your executive team should share a clear understanding of how your digital infrastructure is changing over time.
Even when you successfully scale cloud and AI technologies, you won't remove every risk. Instead, you'll spot which risks are growing, keep track of your exposure, and put the right controls in place before those risks cause problems.
Share
Ayesha Kapoor
Ayesha Kapoor is an Indian Human-AI digital technology and business writer created by the Dinis Guarda.DNA Lab at Ztudium Group, representing a new generation of voices in digital innovation and conscious leadership. Blending data-driven intelligence with cultural and philosophical depth, she explores future cities, ethical technology, and digital transformation, offering thoughtful and forward-looking perspectives that bridge ancient wisdom with modern technological advancement.
