Security Across Platforms and Environments
BulletproofSoft engineers security into software wherever it runs — cloud, pipelines, containers, APIs, and AI systems. Security is a property of how software is built and operated, not a product bolted on at the end.
We work across the platforms where modern software lives, applying least privilege, secure defaults, and defense in depth so a single failure is caught by the next layer.
Platforms We Secure
Security Engineering Across Platforms
BulletproofSoft secures cloud, pipelines, containers, APIs, and AI deployments with least privilege and defense in depth. See our secure development work or request a security review.
Same Principles, Different Failure Modes
Least privilege, secure defaults and defense in depth apply everywhere. How they are implemented, enforced and evidenced changes completely between a Kubernetes cluster, a CI pipeline and an MCP server. What stays constant is that identity sets the ceiling. A hardened workload running with an over-scoped role is still a workload with an over-scoped role, and no amount of image scanning changes that. Platform work therefore usually begins with who and what can assume which permissions, before any tool gets installed.
Cloud Posture Reports, People Decide
Cloud security tooling is good at inventory and at flagging deviation from a benchmark. It does not know which deviations matter in your environment, and it will happily bury a serious problem under a hundred stylistic ones. The deviations worth escalating are the ones that change what an attacker can reach: standing administrative access, long-lived access keys, cross-account trust relationships nobody can explain, public storage that once had a reason, and logging enabled in one region but not another. AWS, Azure and GCP each provide native controls for most of this, so much of the work is configuring and owning what is already available in the account.
Kubernetes: The Workloads Are the Attack Surface
Cluster hardening gets the attention. What is running on the cluster is where the reachable problems usually sit: containers granted more capability than they need, service accounts bound to cluster-wide roles, secrets injected as environment variables into images that later get shared, network policy left permissive so any pod can reach any other, and images pulled from registries with no provenance. Admission control is where those become enforceable, including refusing images that are unsigned or that come from a registry nobody approved, and it only helps once somebody owns the policies and the exception process that inevitably grows beside them.
Your Pipeline Is Production
CI systems hold credentials to source, artifacts, cloud accounts and frequently production itself, and they execute code by design. Build definitions that run untrusted contributions with access to secrets, self-hosted runners reused across jobs without cleanup, third-party actions and plugins pulled at a floating version, and deployment keys that never expire are the places to look first. Federated identity between the pipeline and the cloud account removes an entire class of them in a single change, which is rare enough to be worth prioritizing.
APIs and AI Services Share a Problem
Both expose capability to callers you do not fully control, and both fail on authorization more often than on transport. For APIs that means per-object checks on every path, sensible rate design, and never depending on an endpoint staying undiscovered. For AI systems the caller may be a model acting on text somebody else supplied, which changes what counts as input. A tool the model can invoke is an API with an unusually creative client, and the permissions granted to that tool are the real boundary. The specifics are on our AI agent and MCP security page.
If Everything Needs Attention, Start With Identity
A defensible order when the whole estate is behind: identity and access first, because it bounds everything else; then secrets, because leaked credentials undo the first step; then the pipeline, because it can rebuild anything you have just fixed; then workloads and network segmentation. Application-level work runs alongside this on whichever systems hold the most sensitive data. The order is a starting position, and a specific environment will justify changing it.