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GitHub Advanced Security (GHAS) can find security issues, but a growing alert count is not proof that risk is falling. The business case depends on what happens next: who owns each actionable finding, how quickly high-risk work is handled, and whether closure is verified. Without that follow-through, scanning can consume license and engineering time while leaving exposure in place. That makes remediation targets and accountability central to measuring ROI—not a guarantee that every scan must produce a fix or that scanning has no value on its own.
Why scanning needs a remediation program to produce measurable value
A scan measures detection activity: repositories covered, alerts raised, or checks run. Those figures do not show whether a vulnerable path was fixed, whether a leaked credential was revoked, or whether an unresolved issue reached the default branch. Scanning can still provide visibility, support prevention in pull requests, and help meet governance needs. But if findings are not assessed and acted on, the organization cannot assume that the tool has reduced its exposure.
A remediation service-level agreement (SLA) turns a finding into tracked work by defining an owner, a response or fix target, an escalation route, and an exception process. It also gives security and engineering teams a way to compare intended response with actual outcomes. GitHub documents remediation metrics and security-campaign workflows, but its reviewed materials do not set one universal deadline for fixing alerts. The organization must choose targets that fit its risks and operating context.
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GitHub describes GHAS as two products: GitHub Code Security and GitHub Secret Protection. The current product-page prices in the reviewed material are $30 USD per active committer per month for Code Security and $19 USD per active committer per month for Secret Protection. GitHub billing documentation associates Code Security with features including code scanning, premium Dependabot features, and dependency review; Secret Protection includes secret scanning and push protection.
#1 Best Overall
| Product | Published price in reviewed GitHub material | Included capabilities described by GitHub |
|---|---|---|
| GitHub Code Security | $30 USD per active committer/month | Code scanning, premium Dependabot features, and dependency review |
| GitHub Secret Protection | $19 USD per active committer/month | Secret scanning and push protection |
Those are published list prices, not a promise of what a particular organization will pay. Licensing and billing depend on product, hosting, plan, and billing model; verify the organization’s contract and actual eligible active-committer count. GitHub says public repositories on GitHub.com have code scanning, secret scanning, and dependency review at no charge. Paid Advanced Security is required for private repositories on GitHub.com and for repositories hosted on GHE.com or GitHub Enterprise Server, subject to product and plan rules. GitHub documents active-committer metered billing for GitHub Enterprise Cloud and certain Enterprise Server arrangements, as well as volume or subscription licensing.
For a useful ROI denominator, include the repositories and products actually enabled, plus rollout, alert tuning, triage, remediation, and reporting effort. Product list price alone is not total cost of ownership.
How to measure GitHub Advanced Security ROI
Build a baseline before expanding coverage, then compare it with later results for a consistent repository and alert population. Separate code-scanning alerts, dependency alerts, and secrets: they have different owners and remediation actions. Record changes in scan coverage, alert quality, staffing, product features, and workload so that an apparent improvement is not mistakenly credited to one cause.
Track the alert lifecycle, not just scan volume
- Open findings by severity and age, with production context and repository criticality.
- Rate of new findings, and how many are fixed, dismissed, or still unresolved at merge.
- Time from alert creation to verified closure, plus severity-specific age percentiles and closure rates.
- High-risk findings past the organization’s target, repeated findings by rule or repository, and recurring patterns across teams.
- Coverage: which repositories and languages are scanned, and whether secret and dependency protections are enabled where needed.
GitHub Security overview reports mean time to remediate (MTTR). In GitHub’s documented definition, MTTR is the average age of alerts remediated or dismissed during the selected period, excluding false positives. Because dismissed alerts are included and an average can conceal old outliers, pair MTTR with age percentiles, closure rates, and overdue high-risk counts. Report dismissals separately so a falling MTTR cannot be mistaken for fixes alone.
Set ownership, targets, and escalation
- Assign each actionable finding to a responsible team or person, with a clear escalation path for blocked work.
- Define response and remediation targets using severity, exploitability, deployment status, repository criticality, and patch availability. State who can approve an exception and how it is recorded. These are local policy choices, not deadlines prescribed by GitHub.
- Prioritize critical and high findings while accounting for production context. GitHub’s risk-assessment guidance says high- and critical-risk rules found across multiple repositories merit priority; repeated rules can also point to a systemic training or standards problem.
- Make work visible through pull-request checks, issue links, assignment, security campaigns, and escalation. GitHub security campaigns provide tracking views for spotting stalled work and assessing campaign impact.
- Review performance on a regular cadence against the baseline, then adjust targets or workflow where the backlog repeatedly misses them.
Secrets need a distinct response path. GitHub advises rotating a detected credential immediately; once it has been revoked, removing it from Git history is often unnecessary. That response is different from scheduling a code fix or dependency upgrade.
Translate outcomes into an organization-specific business case
Compare total licensing and operating cost with measured changes in developer remediation effort, time to close high-risk findings, security work displaced or avoided, and risk exposure. A simple ROI calculation can be expressed as (estimated benefits minus total costs) divided by total costs, but the result is only as credible as its inputs. Separate observed labor savings from estimated avoided incident losses, document assumptions, and avoid assigning a cash value to every finding when attribution is uncertain.
Before-and-after comparisons are more informative when they use the same repository and alert population. Note changes in coverage, alert quality, staffing, features, and workload; otherwise, the comparison may reflect those changes rather than GHAS or an SLA. The reviewed material does not establish an independent, product-isolated causal ROI estimate for GHAS or for remediation SLAs themselves.
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What published ROI figures do—and do not—show
Forrester Consulting reported $168.42 million in benefits, $31.63 million in costs, and 433% ROI over three years in a 2023 study commissioned by GitHub. The reported result modeled a composite automotive organization using GitHub Enterprise Cloud and multiple products and services, including Advanced Security, Actions, Codespaces, Dependabot, and code and secret scanning. It is a commissioned, bundled case—not a GHAS-only estimate or a forecast for a typical buyer.
Best Value
In a roughly 2024 guide, GitHub reported a median 28 minutes to automatically commit a pull-request-time CodeQL alert fix, compared with 1.5 hours to fix manually, or three times faster. The result concerns the studied pull-request workflow with GHAS enabled. It should not be generalized to all alerts, an existing backlog, or total program ROI.
GitHub’s risk-assessment page says eligible organizations can receive a free code security assessment covering up to 20 active repositories; most scans finish in 30 minutes, and an organization may run an assessment every 90 days. Eligibility depends on GitHub account and hosting conditions. For a qualified organization, this can offer an initial view before deciding on broader enablement.
These figures can inform questions to investigate, but they do not establish a universal GHAS return or a general reduction in losses from adopting an SLA. Current prices and feature availability can change; check GitHub’s product and billing information and the organization’s own terms before budgeting.
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- Coverage: Are the repositories, languages, secret patterns, and dependency risks that matter actually covered?
- Signal quality: Are alerts actionable, or are false positives and stale findings consuming triage time?
- Workflow fit: Can teams use pull-request integration, assignment, issue tracking, campaigns, and automation with clear ownership?
- Remediation performance: Are high- and critical-risk findings being closed within local targets, and are overdue items or unresolved alerts reaching the default branch?
- Evidence: Do the organization’s own baseline and follow-up results support the cost, or is the case resting mainly on vendor or commissioned-study claims?
If coverage is valuable but remediation performance is weak, first determine whether the constraint is ownership, alert quality, patch availability, or engineering capacity. A larger scan footprint alone will not resolve those bottlenecks. Improve the workflow and measure again before treating detection volume as a return.
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