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The cost of a cloud security failure can be huge, as IBM recently released data showing the average cost is over $4.5 million for companies that experience a cloud data breach due to misconfiguration. Human error continues to be responsible for about 25% of all data breaches, according to Verizon’s Data Breach Investigations Report.
Additionally, according to GitGuardian’s data, 70% of organizations are still storing unencrypted API keys and other secrets directly in their repositories. Additionally, the vast majority of codebases contain significant vulnerabilities with a CVSS score of 7 or higher.
These threats are real today in 2026. In many cases, organizations will sacrifice security for quickness in the deployment process. However, this does not mean that organizations cannot have both speed of delivery and security. Organizations that incorporate secure coding practices into their cloud development processes do so more deeply than simply implementing them after the fact, and ship more quickly and securely.
If you want to secure your infrastructure, here are seven simple and proven security practices that every modern cloud coder should implement to ensure code security. These cloud security best practices are easy to implement once understood from an attacker’s perspective.
This might seem obvious, but most organizations still end up doing it. Hardcoding things like credentials, API keys, or database passwords right in your source code creates a serious security risk the moment you commit that code.
This risk is compounded further when developers fork the repo and share code snippets on an internal wiki or when a former employee still has access to a legacy repository. A single exposed API key can result in total infrastructure compromise, unauthorized access to customers’ data, or large, unexpected cloud bills due to automated crypto mining.
The best way to fix this is to use a dedicated secrets management tool like HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, or Google Secret Manager. These tools let you centralize your credentials, add encryption at rest, set up detailed access controls, keep audit logs, and rotate credentials automatically. GitHub and GitLab just can’t do these things.
It’s much easier to stop a credential from being compromised in the first place than to clean up after one gets compromised. Set up automated secret scanning in your CI/CD pipeline with tools like GitGuardian or TruffleHog, and use pre-commit hooks to keep secrets out of source control. For runtime, inject secrets as environment variables. Never send unencrypted credentials over chat apps like Slack. Even ‘temporary’ credentials usually stick around longer than you think.
With infrastructure-as-code tools like Terraform, AWS CloudFormation, and Kubernetes, you can build your whole cloud setup using code. This makes things consistent and easy to track, but it also means that one mistake in your template can quickly spread to hundreds of cloud resources.
Most cloud security failures come from configuration mistakes. Common issues include identity and access management (IAM) roles that are too open, S3 buckets set to public, databases without encryption, and security groups that allow unrestricted access.
To avoid discovering major problems after deployment, add IaC security scanning to your pull request process. Tools like Checkov, Terrascan, or tfsec can automatically check your templates for security and compliance issues before you create any resources.
Along with IaC scanning, set up policy-as-code using a tool like Open Policy Agent (OPA). This lets you define your security rules in code and automatically block any changes that break your standards. Also, use drift detection to catch cases where someone has changed resources outside of your templates, often as a quick fix.
78% of organizations leave their Kubernetes API servers exposed to the public, creating a major and avoidable security risk. When developers rush to deploy applications, they often assign more permissions than necessary, giving services administrative access they do not need.
The principle of least privilege means giving users, services, or applications only the minimum permissions they need to do their jobs. Nothing more.
Regularly audit your security policies, roles, and service accounts to make sure you follow the principle of least privilege. Use tools from your cloud provider, like AWS IAM Access Analyzer or Azure Active Directory Access Reviews, to find and remove unnecessary permissions and tighten access management where needed.
Do not assign permanent administrative roles. Use just-in-time access so that administrative privileges are granted only when needed and removed after a set period. This limits the window of opportunity for attackers to steal credentials.
When using orchestration tools like Kubernetes, enforce role-based access controls at the namespace level. Require multi-factor authentication for all users accessing cloud environments.
Manual security controls and reviews just can’t keep up with how quickly code is released today. Many companies push out dozens or even hundreds of updates every day. This makes human reviews a bottleneck, and as a result, they often get skipped.
The solution is to build security checks right into your CI/CD pipeline. This way, every time code is committed, built, or deployed, it gets checked for security issues automatically. A good pipeline should include these three types of automated tests:
To keep insecure code out of production, set up your CI/CD pipeline with clear quality gates. If a build has any critical or high-risk security issues, it should fail. For example, you might require zero critical CVEs or make sure there are no hardcoded secrets.
AI tools are being adopted quickly in the software development lifecycle, but this growth brings new security risks. Over 90% of organizations are unprepared for AI-enabled attacks that could expose sensitive data. These risks come not just from outside threats, but also from the AI tools and services used by your own developers.
Shadow AI refers to any AI tools or services that developers use without IT or security review. For example, if a developer pastes proprietary code into a public AI model to debug or uses an unapproved AI autocompletion tool, they may not know how their data is stored or used. Most free AI tools use submitted code to train their models. If your team submits proprietary code or personal data, it could be exposed without your knowledge.
Cloud security platforms help reduce shadow AI risks. A strong cloud and AI security program makes it easier to find Shadow AI use in your development pipelines. You need to know where your code and credentials are stored, including which external services have access.
To manage Shadow AI risks, set clear rules about which AI tools your team can use. Give developers approved, secure tools that protect sensitive data. Make sure your team knows how to use AI tools safely, like cleaning up code and taking out personal information before sharing it with an AI.
Containerisation is now common for many cloud-based applications, but traditional endpoint security does not work well for containers, which are designed to be temporary.
Begin by creating minimal base images for your containers, or use distroless images. Distroless images leave out extra packages, libraries, and debugging tools, which makes it harder for attackers to get into your containers.
Once your container is running, keep monitoring it. Set up runtime security checks and watch for unusual activity, like unexpected outbound connections, changes to the root filesystem, or sudden spikes in resource use. These can be signs of cryptojacking. Always avoid running containers as root. Use kernel security profiles like AppArmor or SELinux to control what containers can do on the host system.
Security is not a one-time task. It is an ongoing process that needs regular attention as new threats and security vulnerabilities appear every day. Even if your software is secure now, a new vulnerability could be discovered tomorrow. Most organizations have at least one security issue that has been around for over a decade.
Set up centralized logging and SIEM tools to collect logs from all your cloud services, applications, and infrastructure. Create alerts for unusual activity, like repeated failed logins or unexpected API use.
Use cloud-native security tools like AWS GuardDuty, Azure Security Center, or Google Cloud Security Command Center with your cloud setup. Keep detailed logs of all changes and access events. These logs help you respond to security incidents and are needed for compliance with standards like SOC 2, GDPR, and HIPAA.
Securing code in the cloud is not about buying every new tool. It comes down to sticking with proven basics that actually address the vulnerabilities attackers look for, like exposed secrets, misconfigured infrastructure, too many permissions, outdated dependencies, and missing monitoring.
The seven practices below give your organization a solid base. If you can manage secrets well, use least privilege, keep Shadow AI in check, and secure your containers, you will cut your risk in a big way. Putting these practices in place also helps your team move faster. If you add automated security checks to your CI/CD pipeline, you will catch problems early, avoid last-minute weekend fixes, and give your developers confidence that the code is secure before it goes out.
A data breach can cost millions and destroy customer trust. So the real question is, can you afford not to put these practices in place? Start with one foundation, get it right, then move to the next. Building security maturity takes time, but each step makes your organization stronger in a tough environment.
