Each of these starts with an “Imagine if…” statement and shows what we’d build to answer it.
These aren’t deployed solutions, but they could be. They’re examples of the kinds of problems we solve. Pick the one closest to what you need. Bring us your own—or let’s work some out together.
FILTER
TRADITIONAL MODEL
- Buy a GRC tool (Archer, ServiceNow, OneTrust)
- Hire consultants to configure and set up
- Build policy and assessment process manually
- Engage a team to conduct assessments quarterly
- Manually chase vendors for responses
- Stale data between assessment cycles
MOSAICAL AI MODEL
- Build a TPRM agent trained on your policy and risk appetite
- Automated vendor questionnaire generation and chasing
- Continuous monitoring of vendor risk signals from your tools
- Risk scoring updated in real time — not point-in-time
- Board-ready vendor risk summary generated automatically
- You own the process, the data, and the outputs
Result: Vendor risk assessed continuously. Time saved: 80% reduction in manual assessment effort. Built from your existing tools and data.
TRADITIONAL MODEL
- Engage a consulting firm for annual assessment
- 6–8 week engagement — interviews and workshops
- Manual evidence collection from multiple teams
- Report delivered — already stale on receipt
- Gap remediation tracked in a spreadsheet
- Repeat the process next financial year
MOSAICAL AI MODEL
- Build an Essential Eight agent using your tool data as evidence
- Automated control mapping from CrowdStrike, Tenable, Wiz, Okta
- Live maturity score updated as controls change
- Gap analysis ranked by business risk and effort
- Remediation tracked automatically — no spreadsheets
- Regulator-ready evidence pack generated on demand
Result: Continuous compliance posture. No annual consulting engagement. Evidence assembled from tools you already own.
TRADITIONAL MODEL
- Engage a SOCI specialist consulting firm
- Months to build the CIRMP documentation
- Manual evidence collection across OT and IT
- Point-in-time compliance status only
- A$643K/day penalty exposure often unknown
- Annual review cycle creates gaps between reports
MOSAICAL AI MODEL
- Build a CIRMP agent connected to your IT and OT environments
- Automated control documentation from live tool data
- Penalty exposure calculated and updated daily
- Gap remediation prioritised by penalty risk
- Always-on evidence collection — regulator-ready on request
- IEC 62443, SOCI Act, Essential Eight mapped simultaneously
Result: Continuous CIRMP compliance. Penalty exposure visible at all times. Replaces SOCI advisor plus MSSP combined.
TRADITIONAL MODEL
- Separate consultants for each framework
- CPS 234, ISO 27001, Essential Eight assessed independently
- Overlapping evidence collected multiple times
- No cross-framework optimisation
- Compliance tracked in five different spreadsheets
- Audit preparation takes weeks each cycle
MOSAICAL AI MODEL
- Build a unified compliance agent across all your frameworks
- Single evidence collection mapped to all frameworks simultaneously
- One fix closes multiple framework gaps automatically
- Live dashboard across CPS 234, Essential Eight, SOCI, ISO 27001, NIS2
- Audit evidence pack generated automatically per framework
- Controls cross-referenced — no duplicate effort
Result: One compliance posture. Five frameworks. Audit-ready on demand. Evidence from tools you already own.
TRADITIONAL MODEL
- Engage risk consultants for annual FAIR assessment
- Weeks of workshops and data gathering
- Risk expressed in CVSS scores or Red/Amber/Green
- CFO and board cannot interpret security risk
- No connection between tool findings and dollar exposure
- Risk register updated manually — stale within weeks
MOSAICAL AI MODEL
- Build a FAIR risk agent connected to your live tool data
- Automated Monte Carlo simulation from your actual environment
- Exposure quantified in AUD — CFO-native language
- Risk scenarios modelled: ransomware, breach, SOCI penalty
- Risk P&L updated continuously as posture changes
- Board brief generated automatically each quarter
Result: Live financial risk quantification. Board answers ‘what would a breach cost us?’ with a real number.
TRADITIONAL MODEL
- Engage Big 4 for cyber due diligence
- 6–8 week engagement minimum
- Limited access to target environment
- Point-in-time snapshot only
- Deal-breaker risks often missed or understated
- Integration cost rarely modelled accurately
MOSAICAL AI MODEL
- Deploy the Security Factory on target environment in 48 hours
- Full security posture scored from available tool data
- Deal-breaker risks surfaced and quantified in AUD
- Integration cost modelled against your existing environment
- Compliance gap analysis across relevant frameworks
- Report delivered before deal close — not after
Result: Target company posture scored in 48 hours. Deal-breaker risks quantified. Integration cost modelled. Replaces Big 4 cyber DD.
TRADITIONAL MODEL
- Tenable and Qualys produce thousands of findings
- Manual triage across multiple tools
- Prioritisation based on CVSS scores — not business context
- Patching teams overwhelmed — no clear order
- Findings duplicated across tools — no deduplication
- Monthly vulnerability report takes weeks to produce
MOSAICAL AI MODEL
- Build a vulnerability agent across all your scanning tools
- Automated deduplication across Tenable, Qualys, Wiz, Snyk
- Prioritisation by asset criticality, exposure and business impact
- Patching roadmap ranked by risk reduction per effort
- Live vulnerability posture — not monthly snapshots
- Business case attached to each remediation decision
Result: 13,000 findings reduced to prioritised decisions. Patching team knows what to fix Monday morning. Business context — not CVSS scores.
TRADITIONAL MODEL
- Annual or quarterly pentest engagement
- Report delivered as PDF — findings in spreadsheet
- Manual triage and remediation tracking
- Evidence file built manually for board or auditors
- Findings lose context between cycles
- No continuous testing between engagements
MOSAICAL AI MODEL
- Build a pentest-to-proof agent on your environment
- Continuous pentest rotation — not point-in-time
- Every finding triaged, owner-assigned, tracked automatically
- Remediation evidence collected and formatted for audit
- Board-ready pentest summary generated each cycle
- Historical trend visible — posture improvement measured
Result: Continuous pentest. Every finding tracked to closure. Evidence file audit-ready on demand. Replaces Mandiant or Bishop Fox retainer.
TRADITIONAL MODEL
- Incident response depends on senior analysts
- Playbooks stored in documents — rarely updated
- Knowledge walks out the door when people leave
- Post-incident reviews done manually — weeks after the event
- No connection between incidents and compliance obligations
- Lessons learned rarely actioned
MOSAICAL AI MODEL
- Build an IR agent trained on your environment and past incidents
- Playbooks generated automatically from your tool data and history
- Institutional knowledge captured and operationalised
- Post-incident review automated — timeline, root cause, remediation
- Compliance implications surfaced automatically per incident
- Lessons learned tracked and deployed to future playbooks
Result: Institutional knowledge preserved. Post-incident reviews automated. Playbooks always current. Team is not the single point of failure.
TRADITIONAL MODEL
- CISO team spends 3 weeks producing quarterly board report
- Manual data aggregation from 15 different tools
- Report stale by the time it prints
- Security language — board cannot interpret it
- No financial exposure figure — just CVSS and RAG ratings
- Board makes risk decisions on incomplete information
MOSAICAL AI MODEL
- Build a board reporting agent on your live tool data
- Six-page quarterly brief generated automatically
- Risk quantified in AUD — CFO and chair can act on it
- Compliance status, active threats, priority actions included
- Updated continuously — never stale
- Translation layer from security language to business language
Result: Board report generated overnight. Risk in AUD. 3 weeks of manual work eliminated. Board finally gets the answers they’ve been asking for.
TRADITIONAL MODEL
- No clear view of which tools are delivering value
- Tool sprawl grows unchecked — budget consumed
- Renewals approved without performance evidence
- Security team cannot justify spend to CFO
- Overlapping tools go undetected
- Annual spend review based on gut feel
MOSAICAL AI MODEL
- Build a tool effectiveness agent across your entire stack
- Each tool measured against actual risk reduction delivered
- Overlap identified — duplicate coverage flagged
- ROI calculated per tool — CFO-grade justification
- Renewal recommendations backed by evidence
- Budget reallocation modelled — where to invest vs cut
Result: Every security tool justified by evidence. Overlap eliminated. Budget reallocated to what actually reduces risk.
TRADITIONAL MODEL
- Annual supplier security questionnaires
- Manual review and scoring
- No visibility between assessment cycles
- Critical suppliers treated same as low-risk ones
- No connection to your own risk posture
- Questionnaire responses taken at face value
MOSAICAL AI MODEL
- Build a supply chain risk agent connected to your environment
- Automated risk scoring from open source intelligence and tool data
- Continuous monitoring — alerts when supplier posture changes
- Critical supplier risk connected to your own exposure
- Assessment frequency matched to supplier criticality
- Evidence collected automatically — audit-ready on demand
Result: Continuous supply chain risk monitoring. Critical suppliers prioritised. No annual questionnaire cycles.
TRADITIONAL MODEL
- Security team drowning in manual reporting and aggregation
- Analysts doing admin work instead of security work
- Capability constrained by headcount budget
- Senior analyst time consumed by routine tasks
- Knowledge concentrated in individuals — fragile
- Burnout and attrition from repetitive manual work
MOSAICAL AI MODEL
- Build automation agents for your highest-volume manual workflows
- Reporting, triage, evidence collection all automated
- Analysts freed to focus on decisions — not data aggregation
- Agentic layer covers routine tasks 24/7 — team doesn’t
- Knowledge captured in agents — not individual heads
- Capacity scales without headcount
Result: Team capacity multiplied. Manual work eliminated. Analysts make decisions — agents do the aggregation.
TRADITIONAL MODEL
- vCISO engaged on a day-rate or monthly retainer
- Limited availability — hours consumed quickly
- Advice based on industry knowledge — not your environment
- No institutional memory between engagements
- Board preparation takes hours of briefing time
- Strategic decisions made without live data
MOSAICAL AI MODEL
- Build a CISO advisory agent trained on your program context
- Strategic advice grounded in your actual environment and data
- Board preparation automated from live posture
- Always available — not limited by retainer hours
- Institutional memory preserved across every engagement
- Decisions backed by evidence — not industry benchmarks
Result: Strategic CISO capability available continuously. Advice from your data — not generic frameworks. Board preparation automated.
Want to see one of these built for your environment? hello@mosaicalai.com