As cyber threats grow more sophisticated, agencies must move beyond static defenses to intelligence-driven security. And while AI and machine learning (ML) enhance efficiency by detecting anomalies, forecasting risks, and accelerating response, agencies still need to address concerns about verification of alert evidence and suggested response actions. AI-powered security integrates with existing tools like Security Information and Event Manage...

Operational technology (OT) is comprised of the systems and devices used to control and monitor physical processes, often in industrial or critical infrastructure environments and utilizing OEM-proprietary equipment. OT systems are designed to manage machinery, equipment, and processes, ensuring they operate effectively and safely, distinct from IT, which focuses on data processing and information management and uses near-universal standards....

State, local, territorial, and tribal governments in 2025 are navigating a complex landscape of tight budgets, evolving cyber threats, ambitious digital transformation goals, and increased demands for transparency – all while being expected to deliver high-quality citizen services. Central to addressing all these challenges is the reliability and performance of agencies’ IT infrastructure. Without a resilient and observable foundat...

The cyber world is becoming increasingly complex as new technologies, policies and expectations come into play. Artificial intelligence (AI) is making inroads into applications, network management, and more, while identity management and zero trust architectures are altering the threat landscape. Agencies are embracing the many varied cloud deployments, such as multi-cloud, hybrid, private, etc. All new technologies introduce new elements of r...

Inserting new technology into the U.S. healthcare system has the potential to remake every facet – from personalized medicine to disease surveillance and response, fraud detection to operational efficiencies. It is a challenging process; legacy technologies that never were designed to be integrated into the larger system, data silos, compliance requirements such as HIPAA, and cybersecurity risks all need to be addressed and modernized. U...

Building a cybersecurity-compliance framework is about more than simply purchasing services from a GovRAMP-certified provider. For instance, GovRAMP itself states that “using an infrastructure with a GovRAMP Authorized status does not automatically make the service provider’s system GovRAMP compliant. Each layer (e.g. IaaS, PaaS, and SaaS) must be evaluated on its own for the provider to obtain a GovRAMP Authorized status.” L...

GovRAMP is all about cybersecurity – built on the National Institute of Standards and Technology’s (NIST) Special Publication 800-53 Rev. 4 framework, based on and similar to FedRAMP, it works on a “complete once, use many” model intended to save both time and money for state governments and CSPs alike. Learning Objectives: Review the elements of cybersecurity addressed by the NIST standard and how they are incorporated...

One consideration for state CIOs is whether to maintain a multi-cloud environment. Having more than one cloud provides the opportunity for state agencies to pick and choose among the different providers for the best fit to meet their needs – but running multiple clouds can be expensive and technically challenging to manage. Learning Objectives: Identify the various criteria that define the differences between CSPs and how agencies can ap...

Cybersecurity has evolved from an afterthought to a fundamental component of modern IT systems. The rise of workflow automation and the rapid expansion of artificial intelligence (AI) applications have significantly enhanced the power, reach, and effectiveness of cybersecurity measures particularly in breach detection and incident response speed. Security Operations Centers (SOCs) play a critical role in safeguarding organizations, serving as...

As cyber threats grow more sophisticated, agencies must move beyond static defenses to intelligence-driven security. And while AI and machine learning (ML) enhance efficiency by detecting anomalies, forecasting risks, and accelerating response, agencies still need to address concerns about verification of alert evidence and suggested response actions. AI-powered security integrates with existing tools like Security Information and Event Manage...