Two metrics matter more than price on day one of a security tool rollout: time to first finding, and time to meaningful coverage. Time to first finding reassures the team that the tool works. Time to meaningful coverage determines whether the investment produces value. Griffin AI and Mythos-class tools have meaningfully different onboarding curves, and the reason is architectural rather than operational.
The four-week onboarding reality
Most enterprise security tools have a common shape across the first month:
- Week 1: connect, ingest, produce first findings.
- Week 2: tune signal-to-noise, fix initial integration issues, set up automation.
- Week 3: expand scope to additional repos/services.
- Week 4: start operationalising — dashboards, SIEM integration, ticketing.
Vendors who compress this compress it by reducing the work in weeks two and three. Griffin AI's structured output and reachability foundation materially reduce both.
Week 1 specifics
Griffin AI onboarding: connect repository, run engine, surface reachability-filtered findings. Typical first-finding time is under 30 minutes for a standard git repo.
Mythos-class tools: connect repository, run analysis, surface LLM-generated findings. Typical first-finding time is comparable — about 30 minutes. The delta starts appearing when the team reviews the initial findings.
Week 2 where the gap opens
Griffin AI's week 2 is tuning, not triage. The reachability-filtered output has a low false-positive rate out of the box; the work is refining the policy set and adjusting reviewer assignments.
Mythos-class week 2 is heavy triage. The team is evaluating hundreds of findings to determine the false-positive rate, deciding which categories to suppress, and tuning prompts or thresholds. The work itself is manageable; the problem is that the tuning is never "done" because every finding still goes through the LLM and the model's outputs drift.
Week 3 scope expansion
Adding repos to Griffin AI is minutes per repo. The engine handles the new codebase with the same policy set; reachability computation runs incrementally; findings flow into the same tracker.
Adding repos to Mythos-class tools is the same minutes of setup but extra days of per-repo tuning, because the noise characteristics vary by codebase and the team has to re-establish signal-to-noise for each.
Week 4 operationalisation
Griffin AI's structured findings flow into SIEM, Jira, and Slack on day one via the API. The SIEM rules are straightforward because finding fields are stable.
Mythos-class tools require per-field parser work for every integration. The LLM-generated fields have some drift, which means the parsers need maintenance. Week 4 often extends to week 8 for the operational layer to land cleanly.
Five onboarding metrics to measure
- Days to first actionable finding
- False-positive rate at 30 days
- Coverage percentage at 30 days
- Mean-time-to-triage at 30 days
- Integrations live at 30 days
Griffin AI benchmarks these across the customer base and shares them during procurement. Mythos-class vendors rarely publish these numbers.
What to evaluate
Set a 30-day deadline for the pilot. Measure all five metrics. Compare vendors not on "number of findings surfaced" but on "number of findings triaged and closed as actionable within 30 days." The latter number is the real onboarding proof.
How Safeguard Helps
Safeguard's engine-plus-LLM architecture compresses onboarding by producing low-noise findings that require less triage, structured outputs that integrate with existing tooling on day one, and scope expansion that doesn't require per-repo re-tuning. Customers report time-to-meaningful-coverage in the first two weeks rather than the first two months. For procurement decisions that care about "how fast does this produce value" more than "how much does this cost," the onboarding curve is the honest answer.