Here are ten examples for badly written or configured DevOps pipelines that endanger data security (across various cloud platforms):

1. AWS S3 Bucket Misconfigurations: Incorrectly configured AWS S3 buckets, such as allowing public read access or not enabling encryption, can lead to sensitive data exposure. In 2017, Verizon suffered a data breach exposing millions of customer records due to an improperly secured S3 bucket.

2. Hardcoded Secrets in Code Repositories: Storing sensitive information like API keys, database credentials, or access tokens directly in code repositories (e.g., GitHub) can expose them if the repository is compromised or inadvertently made public. In 2016, Uber experienced a data breach affecting 57 million users due to hardcoded AWS credentials in their GitHub repository.

3. Insecure Data Transmission in Azure Pipelines: Failing to use secure communication protocols (e.g., HTTPS) or properly encrypt data in transit within Azure Pipelines can allow attackers to intercept sensitive information. In 2021, misconfigured Azure Cosmos DB instances were found to be leaking customer data due to insecure access control.

4. Insufficient Access Controls in GCP Cloud Storage: Improperly configured access controls for GCP Cloud Storage buckets, such as granting excessive permissions or not restricting access to specific IP ranges, can lead to unauthorized access to sensitive data. In 2020, over 200 million Facebook user records were exposed due to a misconfigured third-party GCP bucket.

5. Unpatched Vulnerabilities in CI/CD Tools: Failing to regularly update and patch vulnerabilities in CI/CD tools like Jenkins, GitLab, or Azure DevOps can allow attackers to exploit known security flaws and gain unauthorized access to the pipeline and its associated resources.

6. Insecure Artifact Storage: Improperly securing artifact storage, such as build artifacts or deployment packages, can expose sensitive information or allow attackers to tamper with the deployed applications. In 2018, Tesla's AWS account was breached due to an insecure Kubernetes console, exposing sensitive telemetry data.

7. Lack of Secrets Management: Not using secure secrets management solutions, such as HashiCorp Vault or AWS Secrets Manager, and instead storing secrets in plain text or environment variables can lead to their exposure if the pipeline is compromised.

8. Insufficient Network Segmentation: Failing to properly segment the DevOps pipeline network from other environments, such as production or staging, can allow attackers to pivot and gain unauthorized access to sensitive data or systems.

9. Inadequate Logging and Monitoring: Not implementing comprehensive logging and monitoring mechanisms in the DevOps pipeline can hinder the ability to detect and respond to security incidents or data breaches in a timely manner.

10. Insecure Third-Party Integrations: Integrating third-party services or tools into the DevOps pipeline without proper security vetting or configuration can introduce vulnerabilities and expose sensitive data. In 2019, Capital One suffered a data breach affecting over 100 million customers due to a misconfigured AWS WAF (Web Application Firewall) in their DevOps pipeline.

It's crucial to follow security best practices, regularly review and audit pipeline configurations, and implement robust access controls and monitoring mechanisms to mitigate these risks and ensure the security of data within DevOps pipelines.