A Government Framework for Proving Cybersecurity ROI


This event qualifies for 1 CPEs


Government cybersecurity investments are under greater scrutiny than ever.

 

Agencies must secure mission-critical systems, meet complex compliance requirements and defend against escalating threats while justifying every dollar spent. Yet, traditional ROI models fall short when it comes to measuring the true value of risk reduction and prevention.

 

Join HackerOne and Carahsoft for an exclusive webinar introducing Return on Mitigation (RoM), a novel framework designed to help federal, state and local agencies quantify the financial impact of security programs in terms of avoided loss and measurable return on public investment, not abstract risk scores.

 

This session will feature real-world breach case studies, an actionable RoM methodology and a live demo of HackerOne’s RoM Calculator.

 

You’ll learn how public sector teams can use RoM to:

  • Prevent disruptions to critical infrastructure and public services
  • Protect sensitive data from breaches and ransomware
  • Align investments with NIST RMF, FISMA and FedRAMP
  • Quantify the impact of proactive security measures
  • Justify offensive security program budgets with metrics that resonate across leadership, procurement and audit

Who Should Join

This session is for federal, state and local government professionals responsible for cybersecurity, risk, or budget justification, including:

  • Cybersecurity leaders
  • Risk and compliance managers
  • IT and infrastructure decision-makers
  • Procurement and budget officers

Speaker and Presenter Information

Will Kapcio, Senior Solutions Engineer, HackerOne

Relevant Government Agencies

Other Federal Agencies, Federal Government, State & Local Government


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Event Type
Webcast


This event has no exhibitor/sponsor opportunities


When
Wed, Jul 23, 2025, 11:00am - 12:00pm ET


Cost
Complimentary:    $ 0.00


Website
Click here to visit event website


Event Sponsors


Organizer
HackerOne Government Team at Carahsoft


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