Learn what multi-cloud security is, 2026 best practices, and how in-line tokenization cuts compliance scope across AWS, Azure, and GCP – without code changes.
Enterprises are increasingly distributing their workloads across multiple cloud service providers.
While a multi-cloud architecture improves agility, resilience, and cost optimization, it also leads to fragmented data visibility, inconsistent controls, and growing compliance risks. In effect, the adoption of multiple cloud services requires a strong multi-cloud security model.
Industry research from 2023 (via Wiz) found that 57% of organizations now operate using more than one cloud platform, with 22% using three or more. Yet, over 78% still concentrate 80% of their workloads in a single provider. This shows that while multi-cloud is common, unified data security across the stack remains a challenge.
Moreover, as enterprises adopt more AI-managed services and self-hosted models in the cloud, new data exposure and compliance concerns emerge. For example, 85% of organizations (Wiz) use some form of AI (managed or self-hosted), but the data security controls to protect the data within those platforms are still in catch-up mode. Moreover, new vulnerabilities like DeepSeek’s exposed database and critical GPU flaws highlight how fast innovation can outpace security.
Multi-cloud security is the practice of protecting data, workloads, and applications across multiple cloud environments – e.g., AWS, Azure, and GCP – with consistent data visibility, unified policies, and centralized governance.
Each cloud offers strong native tools, but they generally operate in isolation. Without a proper cloud-agnostic strategy, organizations risk configuration drift, identity sprawl, and inconsistent compliance posture.
Platforms like DataStealth bridge these gaps by protecting sensitive data itself across every environment (including multiple clouds), not just the infrastructure it lives in.
Enterprises are embracing multi-cloud architectures to gain the agility, resiliency, and flexibility that a single provider can’t deliver. In today’s competitive space, no one single cloud platform offers every capability an organization needs. Rather, each cloud service provider brings distinct advantages that align with specific workloads, compliance needs, and innovation goals.
Some specific drivers behind multi-cloud adoption include:
However, while multi-cloud adoption is accelerating, industry research (via Wiz) shows that most organizations aren’t fully there yet. According to Wiz’s analysis of over 200,000 cloud accounts:
This reliance on hybrid cloud setups (i.e., where secondary clouds support only a fraction of total workloads) creates a fragmented security landscape. Many organizations end up managing inconsistent IAM policies, duplicate controls, and uneven visibility across providers.
To succeed, enterprises need multi-cloud security strategies and architectures that scale consistently across both dominant and secondary clouds, therefore unifying policy enforcement, compliance monitoring, and, critically, data protection.
Running workloads across multiple clouds promises flexibility, but it also multiplies complexity. Each provider brings its own security model, management interfaces, and compliance rules or boundaries. Without a unified approach, teams struggle to maintain visibility, enforce consistent controls, and protect data wherever it rests and flows.
Below are some of the leading multi-cloud security challenges enterprises face:
Each cloud provider operates with its own identity, encryption, and logging frameworks. For example, AWS, Azure, and GCP differ in how they handle IAM roles, KMS key hierarchies, and monitoring APIs, thereby forcing security teams to recreate controls in different ways. These inconsistencies make it difficult to enforce least privilege, zero trust, and policy parity across the multi-cloud environment.
Because each platform produces its own logs, telemetry, and alerting, organizations often end up with siloed visibility. Without a single pane of glass, threats that span multiple clouds – e.g., cross-cloud lateral movement or credential misuse – can go undetected. These security blind spots are also a recurring risk in multi-cloud deployments, particularly when teams rely on provider-native tools alone.
Even well-designed environments degrade over time. Industry research (Wiz) found that 47% of companies have at least one database or storage bucket exposed to the internet, typically due to misconfigurations. Attackers scan constantly. Wiz’s experiments show that an exposed bucket with a guessable name can be discovered within 13 hours. With each new cloud service or API release (e.g., AWS added over 1,600 new actions per year), maintaining a consistent posture becomes an uphill battle, even for the most seasoned and well-provisioned security teams.
Multi-cloud means multiple IAM systems, each with unique policies, roles, and privilege boundaries. As identities multiply across clouds, so does the attack surface. Likewise, stale credentials, over-permissioned roles, and inconsistent federation models create opportunities for lateral movement and shadow access paths that posture tools can miss.
Every additional cloud service and integration brings new compliance touchpoints. Frameworks like PCI DSS, GDPR, HIPAA, PIPEDA, and others each define scope differently depending on where and how sensitive data is processed. Without centralized data governance, compliance scope expands and, in turn, drives up audit costs and introduces operational friction.
The rise of AI workloads adds an entirely new class of threats. In general, security is playing catch-up as organizations rush to deploy AI services across clouds. As AI adoption surpasses 85% of organizations (Wiz), multi-cloud security needs to extend beyond infrastructure to cover AI-managed and self-hosted services.
Every new integration – e.g., a SaaS tool, API, or partner platform – introduces new interfaces, credentials, and compliance obligations. In multi-cloud settings, this often means duplicating configurations or rewriting code to meet each provider’s security model. This leads to slower time-to-market, more manual audits, and mounting technical debt.
Overall, modern multi-cloud security practices must go beyond posture management and runtime protection. While these are foundational, organizations should also consider adding data-centric controls that neutralize exposure at the source and, in turn, protect sensitive data wherever it flows, be it across clouds, partners, and/or AI workloads.
A strong multi-cloud security program requires both foundational cloud posture controls and data-centric safeguards that span every provider.
Basically, as organizations scale across AWS, Azure, GCP, and other platforms/services, the following best practices form the baseline for reducing risk, improving visibility, and staying audit-ready.
In multi-cloud environments, attackers exploit gaps between providers. Hence, unified visibility is the first line of defence to detect anomalies spanning multiple environments. However, each cloud provider produces telemetry differently. So, without a unified view, security teams must jump between consoles, thereby slowing detection and response. A centralized monitoring layer consolidates events across clouds, allowing teams to spot cross-cloud threats, correlate incidents, and respond consistently.
By codifying policies centrally (e.g., via IaC templates or policy-as-code), organizations enforce the same baseline security posture everywhere, even as new services are added. Each cloud service provider uses a different policy engine (e.g., AWS IAM vs. Azure AD). So, without standardization, inconsistent configurations emerge, e.g., broader access in one cloud, but stricter rules in another. Unified enforcement ensures no weakest link exists, enabling uniform governance and faster compliance reviews.
Manual reviews can’t scale across thousands of resources and APIs. Therefore, automated posture management provides real-time visibility and ensures security settings stay compliant as your cloud environments grow. Cloud security posture management (CSPM) tools can automatically scan your multi-cloud environment for misconfigurations, drift, and policy violations across clouds. CSPMs can also continuously monitor against best practices (e.g., CIS, NIST, ISO, etc) to identify or highlight misalignments before they become exploitable.
While CSPM tools secure configurations, runtime tools – i.e., cloud workload protection platforms (CWPP) or cloud-native application protection platforms (CNAPP) – defend active workloads from exploits, unpatched vulnerabilities, and malicious activity. This layer detects attacks in progress and, in turn, prevents lateral movement between workloads (e.g., virtual machines, containers, serverless functions, etc) and clouds. In multi-cloud environments, workloads may span across different container runtimes and OS baselines. Hence, CWPP/CNAPP tools ensure consistent runtime defence, even as workloads shift between different providers.
In a multi-cloud world, identity is the new perimeter. So, consistent governance ensures that no cloud becomes an access loophole for attackers. To manage users, roles, service accounts, and permissions across multiple IAM systems, apply cloud infrastructure entitlement management (CIEM) systems. As each cloud manages identity differently, organizations are at risk of identity sprawl (thousands of credentials and roles, many over-privileged or unused). CIEM provides visibility into entitlements, enforces least privilege, and detects risky access patterns.
Organizations should align encryption standards (AES-256, TLS 1.2+) across providers and maintain unified key lifecycle management to reduce operational complexity. That said, while encryption protests confidentiality, it does not protect against exposure. Even encrypted data remains in compliance scope if live secrets exist in systems. Nonetheless, encryption still offers a baseline level of control required by PCI DSS, GDPR, HIPAA, and other compliance standards.
Manual evidence gathering is time-consuming and error-prone. However, automation offers real-time compliance posture and reduces audit preparation from weeks to hours. With an automated compliance platform, you can ensure every change is logged, validated, and reported, which is critical in multi-cloud environments where assets and policies evolve daily.
These seven controls form the foundation of a resilient multi-cloud strategy – i.e., covering visibility, posture, runtime, identity, encryption, and compliance.
However, even with these in place, sensitive data still flows between clouds and systems, often in clear text or encrypted forms that remain in compliance scope.
To reduce risk and shrink audit boundaries, organizations must add data-centric controls – e.g., in-line data tokenization and policy-driven detokenization – to neutralize sensitive data before it enters untrusted environments.
Even the most mature multi-cloud security programs – i.e., those that unify monitoring, enforce least privilege, and automate compliance – share one critical gap: they secure the infrastructure, but not the data itself.
Traditional tools like CSPM, CWPP, and CIEM harden configurations and monitor workloads, but they can’t control what happens to sensitive data as it moves between clouds, partners, and SaaS ecosystems.
In today’s multi-cloud reality, data is constantly in motion:
Each transfer introduces compliance risk and scope expansion. Under frameworks like PCI DSS, GDPR, and HIPAA, any system that stores, transmits, or processes live sensitive data becomes part of your audit boundary.
You can have airtight posture management and runtime security, yet still fail compliance or suffer a breach if raw data flows into unprotected systems. Misconfigurations, partner integrations, or AI workloads can all expose real data that encryption alone doesn’t remove from scope.
To close this gap, organizations need a data access control layer, i.e., a mechanism that governs how and where live data appears, not just who can log into a resource. This is where data-centric security becomes indispensable.
An effective data access control strategy for multi-cloud should:
This approach ensures that most systems handle only non-sensitive tokens, drastically shrinking compliance scope, even as you add new partners, AI workloads, or even cloud providers.
Securing cloud configurations and infrastructure is only half the equation. True multi-cloud security requires protecting the data itself, regardless of where it flows.
The following four controls form the foundation of a data-centric security model that augments posture, runtime, and identity tools. Together, all of these measures ensure that sensitive data is protected, governed, and compliant across every cloud, partner, and SaaS integration.
Traditional encryption protects confidentiality, but encrypted data can still count as “in scope” under regulations like PCI DSS and GDPR. Tokenization goes further by replacing live data with non-sensitive equivalents, meaning downstream systems never handle real secrets.
When it comes to multi-cloud security, where data crosses multiple providers and regions, in-line tokenization ensures sensitive information never leaves controlled boundaries, even if misconfigurations or breaches occur.
Platforms like DataStealth sit in-line within network traffic, i.e., capturing and transforming data as it flows between applications, APIs, and/or clouds. The process is also transparent to existing systems, allowing normal operation without exposing live data. In other words, the live or actual data is replaced by tokens (or masked values) that preserve the format and usability (e.g., in an analytics suite), but the original data is secured in an isolated vault, outside of your clouds.
This approach leads to:
In multi-cloud environments, data often needs to move between regulated regions or third-party services. Without granular control, organizations risk violating data residency and privacy laws. Hence, policy-based detokenization ensures that live data appears only at authorized endpoints (e.g., a payment processor or analytics engine in the correct jurisdiction).
Here’s how it works: When a request to detokenize is made, the policy engine evaluates:
Only if all criteria match the policy is detokenization allowed. Otherwise, the system returns tokens, preventing unauthorized re-identifications.
This solution enforces data sovereignty and purpose-based access, reduces your regulatory exposure across borders, and provides fine-grained control beyond traditional IAM.
Enterprises often run legacy systems and multi-vendor SaaS where inserting agents or rewriting code is impractical or risky. This is where agentless deployment allows for rapid adoption of data protection across cloud, on-prem, and hybrid environments, without slowing innovation or requiring re-architecture.
Solutions like DataStealth deploy as virtual appliances or proxies, integrating into network paths between applications and clouds. No need to modify existing applications or databases, nor add APIs. DataStealth also tokenizes, masks, or encrypts data as it flows, while also maintaining full application compatibility.
This leads to faster time-to-value, lower operational complexity, and future-proof scalability.
Modern multi-cloud security stacks already include powerful tools for posture, runtime, and identity management. CSPM, CWPP/CNAPP, and CIEM platforms all help teams detect misconfigurations, protect workloads, and govern access.
Yet, they all share a key limitation: they protect the infrastructure, not the data.
That’s where DataStealth fits in.
Rather than replacing your existing investments, DataStealth overlays a data-centric security layer across your multi-cloud architecture, tokenizing sensitive data before it reaches cloud resources, controlling detokenization through policy, and automating compliance evidence.
By combining infrastructure-level controls with data-level protection, organizations gain complete coverage, i.e., securing both where data lives and how it’s accessed.
Industry research found that 47% of organizations had at least one publicly exposed database or bucket, and attackers could find them within 13 hours (Wiz). Hence, even the best CSPM can’t stop a breach if live data is inside.
But by pairing CSPM/CNAPP visibility with DataStealth’s in-line data protection, enterprises will achieve defence-in-depth, i.e:
DataStealth is actively deployed by enterprises across multiple industries to solve one of the toughest problems in multi-cloud environments: protecting sensitive data while enabling growth.
Below are real-world scenarios where DataStealth was implemented to reduce compliance scope, accelerate integration, and simplify audits, all without rewriting any code or adding operational overhead.
A leading travel operator needed to onboard a new third-party booking partner into its reservation ecosystem. However, the integration required exchanging payment card data across multiple clouds and APIs. Without safeguards, each new connection would pull additional systems into PCI DSS scope, increasing audit complexity and delaying go-live timelines.
An enterprise marketing team relied on Salesforce Marketing Cloud (SFMC) to manage global customer campaigns. However, data residency regulations (e.g. GDPR, PIPEDA) required keeping customer PII within specific jurisdictions. Moving live data into SFMC risked violating residency requirements and expanding compliance scope across clouds.
Implemented agentlessly, intercepting data flows between internal databases and SFMC.
While the path to multi-cloud security can feel complex, the right partner turns it into a real competitive advantage, i.e., enabling innovation, accelerating integrations, and keeping compliance effortless. If you’re ready to protect sensitive data across every cloud without rewriting code or expanding audit scope, explore how DataStealth can help.
Bilal is the Content Strategist at DataStealth. He's a recognized defence and security analyst who's researching the growing importance of cybersecurity and data protection in enterprise-sized organizations.
