What Is GRC Engineering?

GRC engineering is the application of software engineering, systems thinking, and product thinking to governance, risk, and compliance. In the practitioner definition published by GRC Engineer, practitioners “build pipelines, queries and automated controls that read compliance state directly from the systems that produce it.” The compliance answer is derived from production telemetry rather than asserted by a human in a document.

The discipline has a written charter. The GRC Engineering Manifesto — authored by nine practitioners including Ayoub Fandi, Justin Pagano, Charles Nwatu, Terra Cooke, and Austin Rust — lays out eight values, each phrased as a preference: automation over manual workflows, GRC-as-code over tool-specific constructs, measurable and meaningful risk outcomes over checkbox compliance outputs, evidence and logic over fear, uncertainty and doubt, and in-depth continuous assurance over shallow periodic monitoring.

It also sets out five principles: shift GRC left into system design, practitioners build better solutions than vendors alone, treat GRC as a product rather than a project, make GRC threat-informed, and apply systems and design thinking.

Analyst coverage has converged on the same framing. In a September 2025 analysis, GRC 20/20 Research described the shift as moving from after-the-fact verification to engineered assurance, quoting Fandi: “GRC has to move earlier into design and become machine-readable in how we test, monitor, and gather evidence.”

How Is GRC Engineering Different From Traditional GRC?

Traditional GRC runs on a cycle: audit prep, evidence collection, certification, relief, amnesia, repeat. Controls are described in a document, tested once a year by sampling, and evidenced with screenshots that are stale the moment they are captured. The work scales linearly with the number of frameworks.

That model is measurably straining teams. Research from Swimlane reported in April 2025 found that 54% of respondents spend more than five hours a week on manual compliance tasks, only 39% of the evidence-gathering process is automated on average, 92% rely on three or more tools to gather audit evidence, and just 29% say their compliance programs consistently meet internal and external standards.

GRC engineering inverts the direction of travel. Rather than pushing requirements down from a policy document and reconciling reality against it later, it reads the state of production systems continuously and treats deviations as defects with owners, tickets, and fix times. Here is how the two approaches compare across the practices that matter day to day.

The distinction is not simply buying a tool. Programs frequently automate the wrong layer — faster screenshot collection is still screenshot collection. Our primer on what compliance automation is covers where automation ends and engineering begins.

Where Did GRC Engineering Come From?

GRC engineering emerged from practitioners, not analysts or vendors. Security and compliance leads who had watched DevOps, SRE, and detection-as-code transform adjacent functions started asking why controls were still managed like paperwork. The manifesto formalized that frustration, and the community grew through open-source repos, podcasts, newsletters, and conference talks.

AJ Yawn is one of the most visible figures in that movement. A former SOC 2 auditor, CPA firm partner, and GRC engineering leader at a federal professional services firm, he published GRC Engineering for AWS, a hands-on guide to the practice, and has taught it through labs and workshops. In November 2025 he joined Compyl as VP of GRC Engineering, noting that “GRC finally moves beyond checkboxes and into the same lane as security and engineering.”

One caution the community repeats often: GRC engineering is a capability, not an entry-level job title, and it is not a demand that every compliance analyst learn Python. The GRC Engineer publication is blunt that “Power Automate workflows and systematic spreadsheets are valid GRC Engineering” — what matters is systematic thinking, root-cause analysis, and measuring outcomes rather than activity.

What Does GRC Engineering Look Like in Practice?

The practice breaks into four repeatable patterns.

Policy and controls as code

Control logic is expressed in a machine-evaluable form and enforced where changes happen: infrastructure-as-code scans, Kubernetes admission control, CI/CD gates. The reference implementation is Open Policy Agent, which graduated from the CNCF in February 2021; a survey of more than 150 organizations at the time found 91% used OPA somewhere in their deployment pipeline. A control like “S3 buckets must not be public” stops being a narrative and becomes a rule that blocks the deploy.

Evidence pipelines instead of evidence requests

Rather than emailing the identity team for a user list, a GRC engineer queries the identity provider’s API on a schedule, stores the output with a timestamp and a hash, and maps it to every control that needs it. That reusable unit of evidence logic is the core building block — Compyl’s Evidence Studio calls them blueprints, grouped into framework-specific playbooks. Once the logic exists, adding ISO 27001 to an existing SOC 2 program is a mapping exercise, not a second data-collection project.

Continuous testing and drift detection

Checks run daily or hourly, and a failure raises an alert with an owner rather than a finding in an audit report. This is the operating model behind continuous controls monitoring, and it is the difference between knowing a control worked in March and knowing it works right now.

Machine-readable compliance artifacts

NIST’s Open Security Controls Assessment Language (OSCAL) — described by NIST as “a NIST-led initiative created in partnership with industry to improve and automate security and compliance workflows” — provides standard XML, JSON, and YAML models for catalogs, profiles, component definitions, System Security Plans, assessment plans and results, and POA&Ms. All nine OSCAL models are now released.

Regulators are adopting the same posture. FedRAMP 20x states plainly that “security should be continuously enforced, monitored, and reported — not staged for a point-in-time audit,” and replaces static yearly manual assessments with near-real-time reporting. Its Consolidated Rules for 2026 define 43 Key Security Indicators across ten categories, including requirements such as “persistent testing and validation of changes throughout deployment is automated.” FedRAMP entered Phase 3 in FY26 with the submission pipeline opening in the July–September 2026 quarter.

What Skills Does a GRC Engineer Need?

The technical baseline is narrower than people fear. Most GRC engineering work is querying APIs, transforming JSON, and scheduling jobs, which means Python or a similar scripting language, SQL, REST and GraphQL API fluency, Git, and enough YAML to read infrastructure-as-code. Familiarity with cloud provider config services, identity platforms, CI/CD pipelines, and OSCAL or OPA rounds out the toolkit.

The harder and more valuable half is unchanged from traditional GRC: reading a control objective and knowing what evidence would actually satisfy an assessor, understanding audit standards, and negotiating with engineering teams. The failure mode of a purely technical GRC engineer is elegant automation that no auditor accepts. As the manifesto’s authors argue, architecture comes before automation — scripting a broken process just produces faster chaos.

Demand for the hybrid skill set sits against a persistent staffing squeeze: ISACA’s State of Cybersecurity 2025, published September 2025, found 55% of cybersecurity teams are understaffed and 65% have unfilled positions — which is precisely why leverage through automation matters.

Is GRC Engineering Worth It, and Where Is It Headed?

The economic case rests on two effects: less manual labor per audit, and faster detection when controls fail. IBM’s Cost of a Data Breach Report 2026 puts the global average breach cost at a record $4.99 million, up 12% year over year, and found organizations making extensive use of AI and automation in security saved $1.93 million per breach compared with those using none.

Adoption is following. Gartner’s 2026 Market Guide for DevOps Continuous Compliance Automation Tools projects that “by 2028, 65% of organizations will have integrated compliance automation into their DevOps workflows, reducing compliance risk and improving lead time by at least 25%”, with 75% of those processes leveraging AI.

That is the next inflection. Writing in early 2026, manifesto co-author Justin Pagano predicted that AI in GRC would move from copilots to “full fledged agentic extensions of GRC teams,” with human-in-the-loop patterns and AI-native access management as the necessary guardrails. That direction — systems that gather data, reason about controls, and execute routine work under human approval — is what agentic GRC describes.

The practical starting point is not a rewrite. Pick the three controls that consume the most audit effort, replace their evidence with a scheduled API query, put the query in version control, and run it weekly. You will have done GRC engineering before you have written a strategy deck about it.

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About this article. By Compyl Research. Last updated August 11, 2026. This is general information, not legal advice — consult counsel for your specific obligations. Compyl is an AI-powered, agentic GRC platform built by CISOs.