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Kalispell, Mont. — May 6, 2024
GL Solutions proudly announces their achievement of TX-RAMP Level 2 certification. The TX-RAMP certification simplifies the procurement of the company’s GL Suite regulatory software for those government agencies requiring the certification.
“GL Solutions is committed to exceeding the highest level of data protection standards,” says GL Solutions’ Vice President for Business Solutions Sam Hardin. “Becoming TX-Ramp Level 2 certified marks an important milestone in our continual efforts to provide the best and safest services to our clients. I am excited to share this accomplishment and look forward to continued achievements in data protection.”
TX-RAMP (Texas Risk and Authorization Management Program), developed by the Texas Department of Information Resources, standardizes the approach for “security assessment, certification, and continuous monitoring of cloud computing services that process the data of Texas agencies.” Level 2 certification pertains to confidential/regulated data in moderate or high impact systems.
GL Solutions’ TX-RAMP certification makes GL Suite software easily accessible to regulatory agencies, supporting their modernization efforts. GL Solutions’ Agency Transformation Service offers regulatory agencies a step-by-step solution to modernization. A detailed plan, crafted for each agency, follows the five phases of agency transformation—from the capacity building phase to the model agency phase.
GL Suite is a SaaS, relational database hosted in Microsoft’s FedRAMP-certified Azure environment.
About GL Solutions
This year, GL Solutions celebrates 25 years of helping state regulatory agencies better serve the public with their licensing and permitting software, GL Suite. GL Suite transforms and digitizes operations for scores of agencies and departments nationwide; state agencies from Alaska to Connecticut use GL Suite to improve processes and outcomes.
For more information:
Sam Hardin
Vice President, Business Solutions
Phone: 541-312-3662
Email: hardin@glsolutions.com