Cloud systems have grown into something all development teams use, whether they planned for it or not. Most companies now run their applications in the cloud by default, and the industry keeps moving in that direction. Gartner expects public cloud spending to reach 678.8 billion USD in 2024, which shows just how deeply cloud services have become part of everyday software work. With that growth, the number of cloud security incidents has also increased. Before diving into the mechanics behind those incidents, it helps to see cloud security explained in a way that matches how engineers actually build things. Most cloud breaches start with small, ordinary details that line up in the wrong way.
How Misconfigurations Become Cloud Vulnerabilities
Misconfiguration is still the number one reason cloud environments get compromised. The Cloud Security Alliance has estimated that about 60 to 70 percent of cloud breaches stem from configuration mistakes. Anyone who has built cloud systems will probably nod at that. It usually begins with a simple decision: granting a service an IAM role that is a bit too open or temporarily making a storage bucket public so a test script can read from it. Those small shortcuts tend to survive long after the test is over.
Sometimes the mistake is easier to miss. A developer might leave an early prototype API endpoint exposed. Staging credentials might get copied into a production file because the team is trying to meet a deadline. Automation tends to magnify these issues. When Terraform or Pulumi applies a template across environments, a single overlooked setting repeats itself across everything. A well-known example of this pattern is the 2019 Capital One breach, where a firewall rule and an overly flexible IAM policy created a path that an attacker eventually used. It was not an AWS failure. It was a combination of settings that interacted poorly.
The Path Attackers Follow Through Cloud Resources
If an attacker manages to reach the cloud environment at all, the next steps usually follow a fairly predictable chain. Many attacks begin with leaked API keys. GitHub scans show that millions of secrets end up in public repositories every year, and cloud credentials are among them. CI tokens present another issue. They often carry broader permissions because they need to work across services, so compromising a single CI runner can give access to systems far beyond the original project.
Once inside, attackers move by following whatever permissions the roles give them. In AWS, that might involve assuming one role, then another, and then using that chain to reach storage or compute services. Older EC2 instances running metadata services without the IMDSv2 protections were especially easy to exploit. In many environments, serverless functions become an unexpected stepping stone because they sometimes run with permissions that go well beyond what they actually use.
IBM’s 2023 Cost of a Data Breach Report found that 82 percent of cloud breaches involved data stored in public cloud environments. That number shows why attackers spend so much time looking for exposed storage, misconfigured logging buckets, or routes that reveal more than intended.
Where Engineering Workflows Introduce Hidden Risk
Modern DevOps workflows play a larger role in cloud security than most people realize. Development teams now control everything from build pipelines to deployment automation. This is efficient, but it also means traditional security reviews have less visibility into those systems.
CI and CD pipelines often rely on broad permissions so workflows do not break. GitHub Actions or GitLab runners sometimes inherit privileges far beyond what their jobs require. Plaintext secrets can appear in YAML files, container images, or old commit histories. These mistakes are easy to make because teams are focused on getting builds out the door.
Supply chain threats make the picture even more complicated. Developers pull thousands of packages from open-source registries without inspecting each one. Sonatype’s 2023 report identified 1.2 billion malicious package download attempts in a single year. That number includes NPM typosquatting, PyPI account compromises, and problems involving dependency confusion in projects such as PyTorch. What makes supply chain attacks dangerous is that they often run inside CI pipelines, where they inherit whatever cloud permissions the runner already has.
Large cloud environments also deal with serverless sprawl. A company might have hundreds of functions running briefly throughout the day, each with its own configuration and logs. Combined with multiple Terraform modules, scattered environment variables, and GitHub secrets, it becomes difficult for any one engineer to see the entire system clearly.
Why Threats Go Unnoticed in Cloud Native Systems
Cloud systems generate so much activity that malicious behavior can blend into ordinary operations. Containers spin up and disappear quickly, which means logs can vanish if they are not collected immediately. Serverless functions often provide minimal logging unless teams configure them to capture more details. Larger companies manage thousands of identities, and each one carries its own permissions. Multi-cloud setups spread logs across different providers, which makes correlation a slow process.
In 2024, researchers found Azure storage access rules misconfigured across multiple environments. The issue went unnoticed because logging did not reveal how inherited permissions were interacting. Situations like this happen often. A setting that looks harmless in isolation becomes a problem when combined with the rest of the system.
Cloud breaches rarely come from a single dramatic failure. They come from a chain of small decisions made over days, weeks or even years. Configuration drift, identity sprawl, supply chain issues, and incomplete monitoring combine in ways that are difficult to see without stepping back. Developers often think about each piece in isolation, but cloud security makes more sense when viewed as a workflow. When infrastructure is defined in code, one line in a template can change who controls an entire service. Understanding how attackers move through these systems helps developers build with a clearer sense of how everything connects.
