FEDINSIDER

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

As artificial intelligence continues to advance, legacy security solutions often turn into silos. Privileged Access Management (PAM), Identity and Access Management (IAM) andon-human identities (NHI) all started as specific types of identity solutions. However, they turn into silos as soon as a single workflow runs across them — that is, when an entity, human or machine, holds an identity in each system.

One approach is extending the rigor of privileged access management to every identity that carries privilege: workforce users, workloads, machines and AI agents. Using a next-generation identity security platform to discover, control and govern all identities eliminates the differentiation between access types or levels, removing the silos that leave modern organizations exposed.

Learning Objectives:

• Understand how uncontrolled agentic access can exploit legacy privileged access technologies, turning identity into a primary attack vector.
• Outline the role that identity plays in tracking and recognizing machines and their activities.
• Establish metrics to measure progress toward adopting a single identity security platform across all levels — from users to workload processes, machines and AI agents

U.S. federal agencies manage a broad array of tasks that touch virtually every aspect of Americans’ lives. Civilian, defense, and intelligence agencies are all currently facing requirements to modernize their systems, aimed at providing better user experience and fulfilling their agency’s mission.

Agencies today are navigating a constant stream of change, whether that’s modernizing infrastructure, rethinking virtualization, adopting cloud, defending against evolving cyber threats, or figuring out how to safely operationalize AI. The challenge is twofold: recovering from disruption you can’t prevent while enabling change without creating disruption in the first place.

Learning Objectives:

• Understand operational resilience for organizations navigating infrastructure modernization, virtualization change, cloud adoption, evolving cyber threats, and emerging AI risks.
• Understand how application-centric protection supports the rapid, automated, and orchestrated recovery of mission-critical services while minimizing data loss.
• Evaluate the importance of non-disruptive testing and multi-site analytics and reporting, to ensure readiness for real-world scenarios like cloud adoption, natural disasters, coordinated cyberattacks, and data migrations.
• Explore how agencies can identify and recover from unintended actions by AI agents while maintaining operational control.

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.

A downed aircraft and an urgent facilities work order fail the same way. The published procedure stops answering, and the next step gets rebuilt from memory across shifts, systems and the people who happened to be on.

For the aircraft, that is history in the maintenance system, sister-aircraft records, the tech order, supply status and a cannibalization call made over the radio. For the building, it is the CMMS, the equipment manual, the asset’s repair history, and a dispatch decision made from a phone call. In both cases the hours go to reconstruction before anyone turns a wrench, and the readiness or uptime number does not show the cost until later.

Across the Department of War, federal civil agencies and the contractors who hold their sustainment and base operations contracts, maintenance leaders are examining how decision support can shorten the path from an open problem to an approved action without taking authority away from the person who signs the work order.

Learning Objectives:

• Find where the work stalls: Identify the handoff points where aircraft recovery and facilities work orders lose hours in reconstruction (failed troubleshooting, backordered parts, open engineering requests, work discovered mid-repair) and what each one costs in down time
• Judge a recommendation: Understand what an evidence-backed next action looks like in both domains, including provenance, confidence and the difference between a record and an inference
• Decide for the fleet or the installation, not one asset: Review how cannibalization, controlled exchange and dispatch decisions can account for the second aircraft, the second building and the upcoming requirement before anyone approves them
• Keep the decision with the maintainer: Outline which parts of the work can be assembled automatically, which stay with maintenance leadership, and what a bounded pilot on your own data looks like for one squadron or one installation

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

State, local government, and educational institutions are facing growing demands to modernize IT infrastructure, control escalating virtualization costs, and implement AI securely within strict budget and compliance frameworks.

As “the cloud” has become a fundamental part of IT operations, many state and local agencies actually have moved to a that tailors data, apps and storage to the conditions that provide the security, scalability and cost controls needed to optimize performance.

The challenge becomes maintaining visibility and access across hybrid cloud environments – using public cloud, private cloud, even on-prem infrastructure – through one governed control plane. Providing unified control allows streamlined provisioning and automation across VMs, containers, clouds, and AI-era infrastructure, so organizations can modernize faster, reduce costs, and avoid vendor lock-in.

Learning Objectives:

• Review your agency’s IT environment to determine whether it is a hybrid and, if so, what you have to do to ensure visibility and security across the entire space
• Outline the steps to implement a single control plane, where it will replace existing tools and what has been insufficiently visible to date
• Establish metrics that can evaluate performance and automation requirements for the agency’s entire hybrid environment, including cost savings