As organizations race to adopt AI, modern cloud environments are becoming more complex, distributed, and dynamic than ever before. AI workloads demand elastic infrastructure, access to massive datasets, accelerated compute, and deep integration across cloud services. At the same time, this expansion dramatically increases the attack surface.
Security teams are no longer protecting a single cloud or static perimeter. They are responsible for continuously securing AWS, Azure, and Google Cloud, often alongside Kubernetes clusters, serverless platforms, and AI pipelines. In this environment, security must be continuous, automated, auditable, and cloud-native.
At Pervaziv AI, we aim to be cloud-agnostic and value the diversity that these offerings bring to us. Our team is proficient with a decade of experience in multi-cloud environments. Read on to understand the various components of a unified security platform, which is part of our long-term vision.
A Unified Approach to Multi-Cloud Security
A modern cloud security approach enables organizations to assess, audit, and continuously improve their security posture across providers. Rather than relying on point-in-time reviews, security becomes an ongoing process that spans:
- Cloud infrastructure (compute, storage, networking)
- Identity and access management
- Logging and monitoring
- Kubernetes and containerized workloads
- Compliance and regulatory alignment
- Incident response and forensic readiness
This unified model is critical in the age of AI, where misconfigurations or excessive permissions can expose sensitive models, training data, and inference endpoints at scale.
Core Capabilities for AWS, Azure, and Google Cloud
1. Continuous Security Assessments and Audits
Automated assessments scan cloud environments to identify:
- Misconfigured IAM roles and overly permissive identities
- Missing or weak logging and monitoring controls
- Network exposure such as open ports, insecure firewall rules, or public endpoints
- Encryption gaps for data at rest and in transit
Across AWS, Azure, and GCP, these assessments provide a consistent baseline for identifying risks before they are exploited.
2. Compliance and Regulatory Alignment
Multi-cloud environments often need to comply with multiple frameworks simultaneously. Automated compliance checks map cloud configurations against standards such as:
- CIS Benchmarks (cloud and Kubernetes)
- GDPR
- HIPAA
- PCI-DSS
- NIST and regional frameworks
Instead of manual audits, security teams gain continuous visibility into where they are compliant, drifting, or at risk—an essential capability for AI systems handling regulated data.
3. Incident Response and Forensics Readiness
When incidents occur, speed and context matter. A strong security foundation ensures that:
- Logging is enabled and centralized (CloudTrail, Azure Activity Logs, GCP Audit Logs)
- Critical configuration data is captured and retained
- Evidence required for forensic investigations is readily available
This readiness reduces investigation time and helps teams respond confidently to breaches involving cloud infrastructure or AI workloads.
4. Continuous Monitoring and Security Hardening
Cloud security is not static. New resources are created, permissions change, and services evolve daily—especially in AI-driven environments.
Continuous monitoring enables:
- Regular security scans across accounts, subscriptions, and projects
- Detection of configuration drift over time
- Ongoing hardening recommendations to strengthen cloud posture
This ensures that security keeps pace with rapid innovation.
Making Security Actionable: Dashboard-Driven Insights
A centralized security dashboard turns raw findings into operational intelligence.
Compliance Score
A percentage-based view of how well cloud environments align with selected security frameworks.
- Helps quantify security readiness
- Highlights priority gaps
- Supports executive reporting and audits
Threat Alerts
A prioritized stream of identified risks, categorized by severity:
- Critical
- High
- Medium
- Low
This enables teams to focus on what matters most, especially when AI workloads amplify the impact of misconfigurations.
Resource Health Overview
A real-time snapshot of cloud resources across AWS, Azure, and GCP:
- Identifies unhealthy, exposed, or misconfigured assets
- Supports a holistic view of cloud posture
- Enables proactive remediation
Custom Security Queries
Security teams can tailor views and filters to their needs:
- Focus on IAM, encryption, networking, or specific regions
- Segment by cloud provider, account, or project
- Automate recurring reports and alerts
This flexibility is essential for organizations with diverse teams and workloads.
Historical Analysis
Tracking security posture over time reveals:
- Trends and improvements
- Regressions after deployments or architecture changes
- Evidence of continuous compliance
For AI-driven organizations, this historical insight is critical to proving long-term governance and risk reduction.
Securing Kubernetes and AI Workloads at Scale
Kubernetes is the backbone of many AI platforms, from model training pipelines to inference services. Securing clusters—whether on EKS, AKS, or GKE—requires deep visibility into both Kubernetes and the underlying cloud infrastructure.
Key focus areas include:
Kubernetes Compliance Benchmarks
Automated checks against industry standards (such as CIS Kubernetes Benchmarks) ensure clusters follow security best practices across providers.
Vulnerability and Misconfiguration Detection
Security assessments identify:
- Insecure API server configurations
- Weak or misconfigured kubelet settings
- RBAC roles that grant excessive permissions
- Admission policies that allow unsafe workloads
Severity-Based Findings
Issues are categorized by risk level, helping teams prioritize remediation for vulnerabilities that could expose AI workloads or sensitive data.
Cloud-Native Integrations
Findings can be integrated into native security services such as:
- AWS Security Hub
- Azure security and monitoring ecosystems
- Google Cloud security operations tools
This creates a centralized view of security posture and simplifies remediation workflows.
Flexible Deployment for Modern Cloud Teams
To meet organizations where they are, security assessments can run from:
- Developer workstations
- CI/CD pipelines
- Virtual machines on AWS, Azure, or GCP
- Kubernetes Jobs or serverless containers
This flexibility supports everything from ad-hoc audits to fully automated, continuous security programs.
High-Impact Security Rules
The following security rules focus on repeatable, high-impact risks that security teams must continuously monitor and remediate to safely operate AI-driven cloud platforms at scale. We evaluated several open source tools to evaluate these security rules in a custom sandboxed environment.
Secrets Exposure
Secrets embedded in instance startup or bootstrap scripts represent a critical exposure risk. Initialization data is often accessible via instance metadata services or runtime inspection, making any hardcoded passwords, API keys, or tokens easy targets once a workload is compromised. In AI environments, this can lead to unauthorized access to training data, model registries, or downstream services. The goal is to eliminate secrets from startup logic entirely and retrieve sensitive values dynamically from managed secret stores using identity-based, short-lived access.
Credential Rotation
Long-lived access credentials that remain active for extended periods significantly increase the blast radius of a compromise. Static credentials are difficult to track, commonly reused, and often forgotten as environments scale. For AI workloads that frequently spin up and down across regions and providers, leaked credentials can quietly enable persistent access. The security objective is to enforce regular rotation, favor ephemeral credentials, and adopt workload identities or managed identities wherever possible.
Data Deletion
Data deletion protections are frequently overlooked, even for critical datasets. Storage configurations that allow permanent deletion without additional safeguards make it trivial for compromised credentials or automation errors to destroy training data, model artifacts, or audit logs. This is particularly damaging in AI pipelines where data integrity is foundational. The goal is to introduce layered protections for destructive actions and ensure critical data stores are resilient against both accidental and malicious deletion.
Trust Relationships
Overly broad identity trust relationships create one of the most severe escalation paths in cloud environments. Roles or identities that allow unrestricted or poorly scoped trust can be assumed by unintended principals, including external actors. In multi-cloud architectures, this misconfiguration enables lateral movement across accounts, subscriptions, or projects. The security objective is to strictly scope trust relationships, enforce least privilege, and continuously validate identity assumptions.
Encryption Policies
Weak encryption key policies undermine data protection guarantees even when encryption is technically enabled. Keys with broad access permissions or missing conditions can be used by unintended identities, exposing sensitive data such as AI training inputs or proprietary models. The goal is to apply tightly controlled, auditable encryption policies that clearly define who can encrypt, decrypt, and manage keys across cloud providers.
Secrets Rotation
Secrets that are never rotated represent a silent but persistent risk. Application credentials often remain valid far longer than intended, increasing the impact of leaks or accidental exposure. In AI-driven systems, where services communicate at high frequency, this risk compounds rapidly. The security objective is to automate secret rotation, reduce reliance on static secrets, and favor identity-based authentication for service-to-service communication.
Attack Surface
Unused cloud resources quietly expand both cost and attack surface. Unattached storage volumes, idle compute instances, and reserved network resources provide no business value while remaining exploitable if misconfigured. At scale, these forgotten assets are common entry points for attackers. The goal is to continuously identify and eliminate unused resources, shrinking the environment to only what is actively required.
Network Access
Network access rules that allow traffic from known malicious or high-risk sources dramatically increase exposure. Publicly reachable services are continuously scanned and attacked, and allowing access from known threat ranges accelerates compromise. For AI platforms that often expose APIs and inference endpoints, this risk is amplified. The security objective is to enforce least-privilege networking and continuously validate access rules against threat intelligence.
Data Encryption
Encryption that relies solely on provider-managed keys limits governance and control. While convenient, default keys offer less visibility and fewer safeguards than customer-controlled alternatives. For sensitive AI data and regulated workloads, this can create compliance and audit gaps. The goal is to adopt customer-managed encryption keys that enable stronger access control, logging, and revocation capabilities.
Secrets Leakage
Sensitive data leaking into logs is a surprisingly common failure mode. Debug statements, misconfigured libraries, or verbose logging can unintentionally capture credentials, tokens, or authorization headers. Logs are often widely accessible and retained long-term, turning a single mistake into a persistent vulnerability. The security objective is to enforce secure logging practices and automatically detect and remediate sensitive data exposure in log streams.
Conclusion
In the age of AI, cloud security is no longer just about preventing breaches—it’s about enabling safe innovation at scale. Organizations that embrace continuous, multi-cloud security gain:
- Confidence to deploy AI faster
- Reduced risk from misconfigurations and identity sprawl
- Stronger compliance and audit readiness
- Clear visibility across AWS, Azure, and Google Cloud
As AI reshapes how software is built and deployed, security must evolve alongside it – automated, unified, and deeply integrated into the cloud foundations that power intelligent systems. At Pervaziv AI, we spent several weeks identifying these challenging issues in cloud providers. We have a fair idea on what it takes to be a cloud-agnostic security software vendor.
We are thankful to all three providers – Microsoft Azure, Google Cloud and AWS for their generous cloud credits and for including Pervaziv AI in their Startup programs. We intend to collaborate further with these providers as we continue to innovate.
