Real customer outcomes · Verified deploymentsFederal ATO · CMMC 2.0 · NIST AI RMF · HIPAA MARS-EEvery case is a signed reference call awayReal customer outcomes · Verified deploymentsFederal ATO · CMMC 2.0 · NIST AI RMF · HIPAA MARS-E
Four anonymized SAEGIS deployments across federal government, defense industrial base, financial services and state healthcare. Numbers are the customers' own, verified with their security and compliance leadership before publication.
A cabinet-level civilian agency needed to authorize a new mission system under NIST 800-53 rev5 moderate baseline. Legacy tooling projected an 18-month ATO. SAEGIS delivered signed ATO in 87 days, then transitioned the program to a continuous ATO posture.
The challenge
The system boundary spanned two AWS accounts, a legacy on-prem data warehouse, and three SaaS integrations under the same authorization package. The security control assessor team was working from a 400-tab spreadsheet inherited from the previous ATO cycle.
Evidence collection was manual: screenshots pasted into Word documents, control narratives rewritten every quarter, POA&M lines with no cryptographic tie to the finding that originated them. The Authorizing Official had refused three prior submissions on integrity grounds.
What SAEGIS did
SAEGIS was deployed as the evidence spine across the three integration points, ingesting CI/CD events, cloud-config drift, scanner output, and identity signals. Every artifact — SSP paragraph, SAP task, SAR finding, POA&M row — became a first-class object with an immutable link to its source evidence.
The AI Remediation Assistant drafted control-narrative updates as configurations changed. The human ISSO reviewed each diff before it entered the authorization package. Nothing merged without signature. Nothing signed without evidence.
Outcomes
87days from kickoff to signed ATO
2,140POA&M lines closed or consolidated in the first quarter
0assessor-rejected evidence artifacts on submission
"The evidence spine is what closed the ATO. Every control narrative pointed to the exact commit, scan, or configuration change it referenced. When the AO asked a question, the answer took minutes — not a two-week evidence request."
Defense Industrial Base12 program teams · CMMC Level 2 scope14-month DevSecOps modernization
A prime defense contractor consolidated 12 program-team DevSecOps stacks under a single governance platform ahead of CMMC 2.0 certification. Remediation SLA compliance moved from 41 to 96 percent in nine months.
The challenge
Twelve program teams operated twelve GitLab / Jenkins / Bamboo variants with no shared vulnerability tracking, no consistent remediation SLA, and no consolidated view of CMMC Level 2 control implementation. Program-level cybersecurity risk was invisible to the Corporate CISO's office.
The 2027 CMMC 2.0 assessment window was 18 months away. Independent readiness audits projected a fail at the current maturity level.
What SAEGIS did
SAEGIS deployed as the evidence and governance layer above the existing DevSecOps toolchain — no rip-and-replace. Each program team kept its build tooling; SAEGIS ingested findings, normalized to a common risk score, and enforced the corporate remediation SLA.
A single dashboard gave the CISO real-time control coverage across CMMC's 110 practices, with drill-down to the individual program, repo, finding, and owner. The AI copilot drafted CUI-handling attestations and DFARS 7012 evidence bundles for the readiness auditor.
Outcomes
96%critical findings remediated within SLA (up from 41%)
12→1governance dashboards for the CISO
110/110CMMC Level 2 practices with live evidence at assessment
$3.4Mestimated tool-consolidation savings across the 12 teams
"We didn't have a security problem — we had a dozen security programs pretending to be one. SAEGIS gave us the shared spine we needed without asking twelve teams to change their build pipelines."
Financial Services$42B AUM · 8 AI models in production6-month AI RMF stand-up
A regional U.S. bank stood up a NIST AI RMF-aligned governance program for its AI-assisted underwriting stack in six months, ahead of state-level algorithmic-lending regulation. Model-approval cycle time dropped from 11 weeks to 9 days.
The challenge
Eight production ML models supported credit-decisioning, fraud detection, and customer-service triage. Model risk management was handled by a five-person committee reviewing quarterly PDFs. Model drift, evaluation regression, and prompt-injection resistance for the LLM-powered triage tool were not tracked at all.
State-level algorithmic-lending regulation was imminent. The bank's regulatory affairs team advised that current documentation would not survive the first enforcement action.
What SAEGIS did
SAEGIS mapped the bank's model-lifecycle controls to the NIST AI Risk Management Framework and the AI 600-1 Generative AI Profile. Every model deployment now produces an evaluation scorecard, provenance manifest, red-team result summary, and human-approval trail that lives in the same evidence spine as the bank's existing SOX controls.
The AI copilot drafts Model Risk Management memos from the pipeline artifacts. The MRM committee reviews and signs. The state regulator receives a queryable, timestamped record on request.
Outcomes
11 wk → 9 dmodel approval cycle time
100%production models with live evaluation telemetry
0audit findings on the first state pre-examination
"Regulators do not want a PDF that says we govern our models. They want to see the evidence, in order, with signatures. SAEGIS made that a two-click export instead of a six-week fire drill."
State Government · Healthcare1.9M beneficiaries · CMS-audited12-month HIPAA + MARS-E hardening
A state Medicaid agency modernized its cybersecurity governance across MARS-E 2.2 and HIPAA Security Rule controls. Time-to-detect on privileged-account misuse dropped from 34 days to under 12 hours; the annual CMS audit closed with zero significant findings.
The challenge
The Medicaid system handled protected health information for 1.9 million beneficiaries under MARS-E 2.2 and HIPAA. Continuous monitoring existed on paper; in practice, privileged-account activity was reviewed quarterly by a two-person team drowning in log volume.
The prior year's CMS audit produced 17 significant findings. Remediation of the last cycle's findings was still not complete when the next audit window opened.
What SAEGIS did
SAEGIS unified the privileged-access, endpoint, and cloud-config evidence streams into one continuous-monitoring dashboard. Anomalous privileged-account behavior generated governance events with automatic ISSO notification and 12-hour response SLA.
MARS-E 2.2 and HIPAA Security Rule controls were mapped once, then continuously assessed. The CMS audit team received the evidence package directly from the platform. The team of two became a team of three, doing higher-value work instead of log triage.
Outcomes
34 d → 12 hmedian time to detect privileged-account misuse
0significant CMS audit findings (down from 17)
100%MARS-E 2.2 controls with continuous evidence
62%reduction in overtime hours for the ISSO team
"We used to spend the two months before a CMS audit assembling evidence and the two months after fixing what we found. Now the evidence assembles itself, and we spend those four months on actual security work."
See it in your environment
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Every SAEGIS demo is scoped to your controls baseline (NIST 800-53, CMMC, MARS-E, PCI, ISO 27001 or a custom overlay). 45 minutes with a solutions engineer, not a marketing slide deck.