IAM Misconfigurations Are the New Perimeter Breach.
Cloud environments are not secured by default. We find the IAM privilege escalation paths, misconfigured storage, and lateral movement routes that attackers use — before they do.
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Cloud environments are not secured by default. They are configured by teams under time pressure, accumulated over years of iteration, and rarely audited for the IAM debt that builds up when permissions are granted and never revoked.
IAM misconfigurations are the attack vector in the majority of cloud breaches. Not sophisticated zero-days. Not nation-state tooling. Misconfigured roles, overly permissive service accounts, and privilege escalation paths that have been sitting in your environment since someone added them “temporarily” eighteen months ago.
What We Find
IAM privilege escalation — a low-privilege identity can chain IAM actions (iam:CreatePolicy → iam:AttachRolePolicy → sts:AssumeRole) to reach administrative access. These paths are invisible to policy linters but exploitable by any adversary who enumerates your IAM structure.
Exposed storage — S3 buckets, Azure Blob containers, and GCS buckets with public access, overly permissive bucket policies, or cross-account access misconfiguration. We enumerate all storage resources and test actual access, not just policy analysis.
Secrets in infrastructure — API keys, database credentials, and service account tokens in EC2 instance metadata, Lambda environment variables, Kubernetes secrets, and CloudFormation outputs. We enumerate all secrets management paths and test for exposure.
Lateral movement paths — once an adversary has an initial foothold (compromised Lambda execution role, breached developer credentials), what can they reach? We simulate the full lateral movement chain from every realistic initial access point.
AI Workload-Specific Risks
AI and ML workloads in cloud environments have a particular security profile: they require access to large datasets, model artifacts, and often have internet egress for training. Service accounts provisioned for SageMaker notebooks, Vertex AI pipelines, and Azure ML compute clusters frequently have broader permissions than necessary — and those permissions are the blast radius of any compromise.
We specifically assess AI workload permissions, model artifact access controls, training data access paths, and API key exposure in ML pipelines as part of every cloud penetration testing engagement.
Engagement Phases
Cloud Asset Discovery
Complete cloud asset inventory, IAM policy analysis, service configuration review, external attack surface enumeration, exposed storage discovery.
IAM & Permission Testing
IAM privilege escalation path analysis, role chaining assessment, service account permission mapping, cross-account access review.
Lateral Movement & Data Access
Lateral movement path simulation from compromised identity, data exfiltration path mapping, secrets management review, logging and detection gap analysis.
Reporting
Cloud attack surface map, IAM privilege escalation findings, misconfiguration inventory, remediation roadmap with priority ranking.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| IAM Coverage | Compliance scan — policy review only | Full privilege escalation path analysis |
| Attack Simulation | Theoretical risk assessment | Demonstrated lateral movement from compromised identity |
| AI Workload Coverage | AI/ML services not included in standard cloud review | SageMaker, Vertex AI, Azure ML, Bedrock included |
Tools We Use
Frequently Asked Questions
Which cloud providers do you test?
We test AWS, Microsoft Azure, and Google Cloud Platform (GCP) individually or in multi-cloud configurations. We also assess cloud-native AI services: AWS Bedrock and SageMaker, Google Vertex AI, Azure AI Studio, and any custom AI workload deployed in these environments.
How do you test without disrupting production?
Cloud penetration testing is primarily read-only IAM and configuration analysis. We identify privilege escalation paths and misconfigurations through policy review and safe enumeration — we do not exploit findings in ways that modify, delete, or disrupt production resources without explicit written authorization. Any active testing (privilege escalation demonstrations) uses isolated test accounts or explicit authorization for production-safe actions.
What permissions do you need?
For AWS, we require a read-only IAM role with SecurityAudit and ReadOnlyAccess policies — equivalent to what your internal audit team would use. For Azure, Contributor or Reader role at subscription level. For GCP, Viewer role at project level. We provide exact permission requirements in the scoping document.
Do you test AI and ML workloads?
Yes. AI/ML workloads (SageMaker notebooks, Vertex AI pipelines, Azure ML compute) often have broad service account permissions because they were provisioned for development convenience. We specifically assess AI workload permissions, model artifact access controls, training data access, and API key exposure in ML pipelines — vulnerabilities that standard cloud security assessments miss.
Find It Before They Do
Book a free 30-minute security discovery call with our AI Security experts in Dubai, UAE. We identify your highest-risk AI attack vectors — actionable findings in days.
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