George Washington University (GWU)

Recent compromises of municipal water systems, including attacks targeting internet-connected programmable logic controllers and remote cellular modems, have highlighted the evolving cyberthreat landscape facing state and local utilities. Similar risks extend across operational technology environments supporting water, power and other essential public services.

Protecting these systems requires more than securing individual devices or networks. A defense-in-depth approach can help utilities control interactions across converged IT and operational technology environments, limit lateral movement and prevent a compromise in one system from spreading to other mission-critical technology.

Learning Objectives:

• Identify Utility Infrastructure Risks: Examine common attack vectors and lateral movement paths across water, power and related operational technology environments.
• Strengthen IT/OT Defenses: Explore ways to map dependencies, improve visibility and apply OT-safe microsegmentation and Zero Trust controls to sensitive systems.
• Improve Resilience and Compliance: Learn how automated containment can help maintain operations during an incident while supporting alignment with federal cybersecurity guidance and performance goals.

Integrated Sensing and Communication (ISAC) is a 6G technology that enables wireless networks to simultaneously transmit data and detect objects, essentially turning mobile networks into radar networks.

ISAC uses shared radio signals, infrastructure, and hardware to track, locate, and image objects in real-time, improving network efficiency and enabling new, high-precision services. It reduces hardware costs and saves spectrum resources by consolidating functions. It is considered a cornerstone of 6G, enabling faster, more reliable communication.

Learning Objectives:

• Identify the system requirements to incorporate ISAC into your agency’s edge devices
• Delineate steps to maximize AI use in ISAC-enabled networks, including assessment of available databases and their cleanliness
• Determine what additional information needs to be gathered to utilize ISAC capabilities

AI success is often isolated, limited to pilot efforts that tackle one specific challenge or workflow. AI pilots are most focused on getting the technology right; implementing an enterprisewide AI strategy requires alignment across leadership, workforce, procurement, and mission teams, though definitions of success, ownership, and ROI remain unclear.

How agencies can move beyond the isolated success in a pilot and achieve similar results across their organizations is more a strategic challenge than a technical one. It requires shifting from a use case to capability, from technology experiment to an operational effort, by prioritizing scalable infrastructure, robust data governance, and change management.

Learning Objectives:

• Identify organizational and work culture barriers that need to be addressed
• Review your agency’s budget and procurement activities to ensure funding is available as scaling up takes place
• Delineate your agency’s automation and AI priorities to maximize both effectiveness and impact

Today’s advanced 5G networks are the essential first step to create an intelligent digital fabric. They provide higher upload speeds and open network interfaces for developers to use. They also make networks more programmable and easier to automate.

While the future may be 6G, investments made today into building out 5G and 5G standalone (5G SA) networks will not be stranded; they enable AI apps today and will be incorporated into the rollout of 6G tomorrow.

Learning Objectives:

• Identify the system requirements to incorporate ISAC into your agency’s edge devices
• Delineate steps to maximize AI use in ISAC-enabled networks, including assessment of available databases and their cleanliness
• Determine what additional information needs to be gathered to utilize ISAC capabilities

Thought leaders from government and industry discuss how several innovative trends are combining to change the way agencies deliver services

Innovation Summit 2026 – AI Dominates the Landscape

ChatGPT was officially released by OpenAI on November 30, 2022, launched as a free public research preview. It quickly became one of the fastest-growing technology products in history and accelerated the release of competing generative AI platforms across the market.

Federal agencies are following directives such as the Office of Management and Budget’s “Accelerating Federal Use of AI through Innovation, Governance, and Public Trust.” According to the Federal Agency Artificial Intelligence Use Case Inventory, 56 federal agencies reported AI use cases at all stages of development. It shows that the number of submitted use cases rose from 1,757 in 2024 to 3,611 in 2025 – of which 1,818 have been deployed or piloted.

Learning Objectives:

• Scale AI from pilot programs to enterprise adoption
• Build governance, security, and Zero Trust frameworks for AI
• Strengthen workforce readiness, change management, and mission measurement
• Modernize data, infrastructure, and legacy environments for AI workloads
• Improve resilience across hybrid, multi-cloud, edge, and distributed operations
• Address emerging AI threats, security risks, and operational challenges

AI investment is exploding, but most agencies struggle to prove they are delivering measurable business outcomes. Administrators are frustrated with technical metrics that do not translate into terms that describe customer-facing improvements.

Quantifying the benefits and return on investment (ROI) is so difficult because agencies often lack clear KPIs and may face an initial dip in productivity before value is realized. Time savings or employee productivity gains are often qualitative and hard to translate into financial benefits.

Enterprise Architecture teams, meanwhile, are connecting AI initiatives to service strategies, value streams, customer journeys, and measurable outcomes – and generating what credible service improvement looks like.

Learning Objectives:

• Review the underlying reasons why tracking gains from AI use are so difficult, such as a lack of defined metrics in advance or hidden bottlenecks that can be caused in one department by increased output in another department
• Understand how your EA teams are measuring performance outcomes and connecting them to business impacts

Federal agencies, along with state & local governments and education (SLED) institutions are facing growing endpoint complexity, expanding cyber threats, and increasing pressure to do more with limited resources and budget. Today’s IT environments span numerous operating systems, device types and locations, creating operational blind spots that make it difficult to maintain security, compliance and a productive digital experience for end users.

At the same time, the pace of vulnerability discovery, patching requirements and AI-driven threats has outgrown what manual processes can effectively manage. Fedeal and SLED organizations need a new approach, one that combines trusted visibility, intelligent automation and human oversight to proactively reduce risk and improve operational resilience.

Learning Objectives:

• Understand the need for a new approach to autonomous endpoint management in a time of AI agents that you and your team may not know about.
• Evaluate the differences between autonomous endpoint management and device management to recognize the role each plays in hybrid system security.
• Identify all the tools your organization has acquired over time to address individual problems and how they can be consolidated to build a more unified approach to security management.
• Explore how Federal and SLED organizations like yours are using Autonomous Endpoint Management to modernize operations, improve compliance and build a more resilient cybersecurity posture.
• Understand the role of governed autonomy including how organizations can adopt AI-driven operations while maintaining oversight, accountability and public trust.

Artificial intelligence is changing the financial risk landscape for state and local government. As AI makes it easier to create convincing receipts, invoices, vendor documentation, and other financial records, traditional controls and manual review processes may be increasingly challenged to identify what is legitimate, what is an error, and what could signal fraud.

At the same time, AI and automation are giving government finance teams new ways to strengthen oversight. By identifying unusual spending patterns, flagging potential policy violations, detecting anomalies earlier, and focusing attention on higher-risk transactions, emerging technologies can help organizations move from reactive review toward more proactive financial controls.

Learning Objectives:

• Understand how AI is changing the nature and sophistication of financial fraud
• Identify where manual processes and traditional controls may leave gaps in detection and oversight
• Outline how AI and automation can help identify anomalies, policy exceptions, and potential fraud earlier
• Evaluate ways to strengthen financial controls without adding unnecessary administrative burden
• List key considerations for balancing AI-enabled capabilities with governance, transparency, and human oversigh

Historically data storage has been considered a rather stodgy topic, all about capacity, retrieval speed, and data organization. But as AI data demands grow and change, the role of storage has evolved. This makes sense as AI risks and incidents are becoming more common, and agencies with invaluable data assets recognize they are significant and pose an ongoing challenge for federal and state agencies.

Today storage is rapidly becoming an active data layer where the value of data is realized and having the right data foundation is key for activating an AI strategy

As deployments become more agentic, concurrent, and context-intensive, the data foundation must do more than store information. It must deliver data fast enough to keep expensive AI infrastructure productive, protect essential information against disruption or loss, and help agencies put more of their existing data to work.

Learning Objectives:

• Build a stronger data foundation for scaling public-sector AI initiatives.
• Improve the value of AI investments through greater performance and efficiency.
• Strengthen operational resilience by protecting and recovering critical AI data.
• Unlock greater value from existing data, including protected historical information, for future AI initiatives.

There is a natural tension between preparing for worst-case scenarios and avoiding unnecessary alarm. CISA promotes joint preparedness across cyber and emergency management communities; you can help shift the focus from recovery to restoring public and stakeholder confidence when systems are back online.

Join us as thought leaders from government and the private sector discuss the big picture – that preparing for a cyber attack at a National Special Security Event, responding to that attack, and working to restore confidence – and answer your questions. As modern airports integrate critical Operational Technology (OT) – from baggage handling and fueling to lighting systems – with traditional IT networks, the legacy concept of the “air gap” as protection has vanished.

High-profile incidents at major hubs like SeaTac and Heathrow highlight how cyberattacks attacks lead to large-scale disruptions of physical operations. Compounding these operational threats, aviation leaders face stringent regulatory pressure to meet TSA Security Directives (SD 1542-23-01), CIRCIA 72-hour incident reporting windows, and NIST SP 800-82 standards.

Learning Objectives:

• Discover how microsegmentation neutralizes lateral movement and isolates administrative IT from safety-critical OT environments to prevent ransomware and phishing attacks from escalating into airport-wide outages
• Learn strategies to mitigate third-party and supply chain risks, secure vendor API connections and shared platforms, to contain breaches originating from airline partners, concessionaires, or third-party service providers
• Understand how to streamline regulatory compliance by aligning your network architecture with TSA SD 1542-23-01 mandates and building the visibility needed to satisfy CIRCIA and NIST reporting requirements