
AI accountability and documentation layer that verifies your software development lifecycle was actually followed and generates audit-ready evidence for SOC 2, SSDF, and CMMC.
A startup from Canton, the United States that is founded by Anthony Garrett.
This page is designed to help you find out whether SDLC Playbook is good and if it is the right choice for you.
SDLC Playbook is the accountability and evidence layer for software teams. It connects to GitHub, Jira, or Azure DevOps and continuously checks whether the process you say you follow is the one you actually ran: every pull request scored for code review, a linked requirement, and test evidence; every release assembled into an audit-ready package as a byproduct of shipping it.
What it does
Who it's for
Any team that ships software and has to prove how it was built, from 3 seats to 300. It is used across the whole delivery process, not just engineering: product managers, project managers, QA, compliance, and the CEO or CIO who signs the attestation.
SDLC Playbook is not a coding assistant. It is the governance layer that sits underneath them, including for teams whose code is increasingly written by AI.
Pricing is per seat with annual or monthly billing. A 90-day free design partner program is open.
Listed in
Integrations
GitHub, Jira, Azure DevOps, Slack
Compliance Support
SOC 2, NIST SSDF (800-218), NIST 800-171, CMMC
Audit Readiness
Auditor-ready evidence package with PDF and signed ZIP manifest
AI Agents
Requirements Author, QA Strategist, Requirements Auditor with Jira write-back
SDLC Playbook is not a coding assistant and not a generic compliance checklist. It is the accountability layer that sits on GitHub, Jira, or Azure DevOps and continuously verifies that the process you say you follow is the one you actually ran. Every pull request is scored for code review, a linked requirement, and test evidence. Every release is assembled into an audit-ready package as a byproduct of shipping it, with citations back to the PRs, tickets, and pipeline runs, instead of a spreadsheet rebuilt the week before the auditor arrives. It covers the whole delivery process, not just engineering, so product, QA, compliance, and the executive who signs the attestation all see the same evidence.
Compliance automation platforms like Vanta, Drata, and Secureframe are built around infrastructure and policy controls: is MFA on, is the laptop encrypted, was the policy signed. They treat the software development process itself as a checkbox. Engineering analytics tools like LinearB and Jellyfish measure speed and throughput, not whether the required steps happened.
SDLC Playbook fills the gap between them. It scores the actual work at the moment it happens: was this PR reviewed, is it tied to a requirement, does it carry test evidence, did the release clear every gate. That evidence is captured continuously and mapped to SOC 2, NIST SSDF, NIST 800-171, and CMMC controls, so audit week stops being a reconstruction project. It also includes AI agents that draft user stories, test plans, and requirements audits with write-back to Jira, which none of those tools do.
It is priced per seat with annual or monthly billing and works for a three-person shop as well as a 300-seat organization.
Any team that ships software and has to prove how it was built. That is usually a company facing SOC 2, a serious customer security questionnaire, or a federal contract that requires SSDF or CMMC attestation, in industries like healthcare, insurance, fintech, and government contracting.
It is not only an engineering tool. A seat is anyone who works in the software development lifecycle: engineers, product managers, project managers, QA, compliance and GRC staff, and the CTO, CIO, or CEO who signs off on releases. Teams range from three seats to several hundred. A common trigger is hiring a first GRC manager or a new engineering leader and not wanting them to inherit the audit spreadsheet.
The founder spent 25 years running software engineering in healthcare, insurance, and federal, and lived the same cycle at every stop: the process existed on paper, the work happened in GitHub, Jira, and CI, and every audit meant someone rebuilding a spreadsheet of screenshots and ticket IDs by hand to prove the two matched. The evidence always existed. Nobody captured it at the moment it was created, so it had to be reconstructed later, by the most expensive person on the compliance side.
AI-written code made that worse, not better. Teams now ship at several times their old velocity, and the validation side is still bounded by human hours. SDLC Playbook was built to close that gap: a process that produces its own proof, so the audit is a byproduct of shipping rather than a scramble before the auditor shows up. It launched in 2026 from Canton, Georgia, and is currently onboarding design partners.
We have collected here some useful links to help you find out if SDLC Playbook is good.
Check the traffic stats of SDLC Playbook on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of SDLC Playbook on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of SDLC Playbook's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of SDLC Playbook on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
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