Platform engineering is fundamentally changing how enterprises handle compliance by moving away from reactive, manual audits toward automated, policy-driven systems.
Embedding governance into infrastructure pipelines creates scalable, real-time compliance, ultimately turning regulatory requirements into seamless, built-in features of the modern engineering ecosystem.
These controls are typically embedded in the DevOps pipelines they create for their consuming development teams.
“These are often referred to as paved or golden paths, where compliance is built into the pipeline so developers can adhere to compliance controls without even knowing they are in place,” explains Jim Mercer, IDC program vice president, software development, DevOps and DevSecOps.
Compliance may include application security testing (AST) and might include SAST, Secrets Detection, and SCA. Compliance can also extend to the supply chain itself, building SBOM for dependency management, artifact signing, and attestation to capture metadata.
Mercer says a good benchmark is Supply-Chain Levels for Software Artifacts (SLSA), but adds companies may also need to consider other compliance standards, such as CIS Benchmarks, NIST, and DISA STIGs.
Additional organizational compliance requirements, such as HIPAA, SOC 2, PCI, and others, may require specific checks and data audits throughout the DevOps pipeline.
Automated Compliance, PaC Benefits
Mercer explains that in platform engineering, the goal is to transform compliance from a reactive “checkpoint” into a continuous service.
“Automation is best suited for high-velocity, objective technical checks, while manual audits might be needed for high-level governance and nuance,” he says.
He adds that a best practice is to automate as much as possible to reduce friction, improve velocity, and avoid the risk of human error.
Meanwhile, Policy-as-Code (PaC) fundamentally shifts governance from a static, reactive process to a dynamic, proactive service.
Mercer explains that in a traditional model, governance often relies on manual review of spreadsheets or PDFs, creating a “compliance tax” that slows down engineering teams. Many modern PaC tools use declarative languages (like Rego or CUE) to define rules.
“By codifying these rules, organizations can enforce governance with the same speed and rigor they use for software delivery,” Mercer says.
Velocity, Efficiency Metrics
To assess whether compliance is truly becoming continuous and real-time, Mercer points to DORA-related metrics or velocity and efficiency metrics, for example the percentage of deployments that pass automated compliance checks on the first attempt.
“A low ratio indicates that your Golden Paths are not well-aligned with developer workflows,” he says.
Another might be the lead time for compliance approval, measuring the time from when code is committed to when all automated compliance gates are cleared.
“In an effective platform engineering scenario using a paved path, this should be measured in minutes, not days,” Mercer explains.
Risks, Blind Spots
When compliance moves from a human-led checklist to a set of silent automation, the nature of failure changes from “missing a step” to “systemic blind spots.”
Mercer cautions that automation only checks for the specific patterns it has been programmed to see, while over-reliance on existing templates can lead to a “set it and forget it” mentality, resulting in stale compliance logic.
“However, more intelligent AI-driven compliance provides the promise of managing the nuances that static code-based policies might miss,” he says.