George Washington University (GWU)

Government agencies are pushing toward a more agile, high‑tech future. It requires using AI to eliminate time‑intensive operational work, strengthen foundational service and operations data, and accelerate mission‑critical functions without compromising security or compliance.

Public sector organizations are now approaching AI as an operational capability rather than a pilot, including the decisions required to retire legacy processes, establish governed data foundations, and adopt new ways of working. The challenge is moving beyond traditional “Generative AI” (which only provides answers) to “Agentic AI,” which can autonomously perform multi-step tasks, navigate disparate systems, and resolve issues with human-in-the-loop oversight.

Learning Objectives:

• Outline your agency’s data readiness for AI and what will constitute foundation data for AI applications
• Delineate the steps involved in retiring legacy processes within your agency
• Identify processes where agentic AI can streamline the steps and boost productivity

The Government Accountability Office (GAO) estimated in a 2025 report that the U.S. government loses between $233 billion and $521 billion annually to fraud. In its follow-up report and accompanying blog post, the watchdog agency “found that agencies still struggle with understanding whether their efforts work” to prevent fraud.

A combination of approaches provides the strongest protection against fraud, including tools to find weaknesses in internal controls, processes to assess fraud risks on an ongoing basis, analyses that determine the savings generated by antifraud investments – both justifying spending on antifraud protections and encouraging additional investments – and using AI to generate insights into emerging fraud risks or to aid advanced fraud detection.

Learning Objectives:

• Identify steps to create a top-down culture of integrity and ethical behavior
• Establish a framework for regular, thorough fraud risk assessments to identify vulnerable programs and processes
• Delineate necessary automated and manual controls, such as dual authorization for transactions, and strict access controls
• Outline training and awareness requirements for employees to identify red flags

It has taken very little time for AI’s risks to emerge. According to Verizon’s 19^th annual Data Breach Investigations Report, AI is fundamentally remodeling cyber security in real time, for everyone to see.

The benefits of AI take longer to manifest. Today’s government agencies find themselves managing a hybrid AI approach, balancing a mix of hosted provider models (like OpenAI and AWS Bedrock), open-source self-hosted models, and organization-specific fine-tuned models. When an organization scales AI, they don’t just use one model; they use a hybrid ecosystem. This is one area where AI’s protective capabilities can come into play.

Learning Objectives:

• Assess how your organization governs user access and identity controls across multiple AI models, platforms, and services within a Zero Trust framework
• Identify gaps in visibility, monitoring, and policy enforcement across AI endpoints, APIs, and models, and outline strategies to improve consistency and oversight
• Delineate safeguards and access controls that help prevent sensitive data exposure when employees and contractors interact with external AI services and generative AI tools

Government agencies at all levels – federal, state, local, tribal, territorial – are wrestling with ways to improve physical security through, and in collaboration with, cyber assets. This intersection of physical security and digital surveillance in government – often called cyber-physical security convergence – represents the merging of traditional facilities protection with digital IT and OT (operational technology) networks and AI tools.

By linking smart cameras, biometric access points, and IoT sensors, agencies can create unified threat detection that monitors real-world environments while compiling vast amounts of trackable, searchable data. Some cities and counties, for instance, have created real-time crime centers (RTCCs). Healthcare facilities and educational institutions, intelligence agencies, even social services agencies, also are exploring how to combine physical and cyber security.

Learning Objectives:

• Identify the components of both physical and cyber systems used for facility access and security
• Review tools that can work with these disparate elements to create a unified surveillance and security system
• Outline ways that real-time crime centers have pioneered the use of data, including images, to anticipate, prevent, and solve criminal activities

Agencies have launched dozens of AI pilots – but many never make it into production. They stall for many reasons, including integration challenges, policy friction, unclear ROI, and the difficulty of embedding AI into real mission workflows.

It is relatively easy to set up a pilot program – target a particular process, make sure the datasets are clean and accessible to your AI tools, upgrade key hardware and software, and test AI-driven outcomes for quality results. But the same steps are arduous and time-consuming when scaling up; untrustworthy data, out-of-date or obsolete infrastructure, and poor identity control systems, to name a few factors, all must be addressed before an AI pilot can be scaled across the enterprise.

Learning Objectives:

• Outline the elements that were improved to start the pilot and assess how much upgrading and improvement is needed to expand the pilot across the agency
• Assess the talent available within the agency to implement an enterprise-wide AI program and the hiring needed to execute
• Delineate budget requirements and whether performance improvements can generate savings that can be applied to expand the program

Federal grants management has always been a complicated process. The Government Accountability Office (GAO) found in a newly-released report that “[w]hile there are certain standard requirements, each grant program has different authorizing legislation and may also be subject to agency-specific regulations and guidance. For some aspects of grant design and administration, agencies also have more discretion to make decisions.”

As a result, the design and administration of grant programs across federal agencies vary substantially, creating friction at every step and making compliance by grantees an intense, often unmanageable process. Many participating organizations designate a CFO to manage them; many mid-sized and smaller companies, without the financial resources or manpower to manage them, don’t attempt to apply because of the burden.

Learning Objectives:

• Identify the points of friction in your agency’s grants administration processes
• Evaluate the grantee applicant pool from which your agency makes awards to determine how to make it broader and deeper
• Delineate where in the grant application process and the administration process technology can be used to mitigate or eliminate chokepoints

AI is rapidly transforming our world—from scientific discovery to business operations to national defense. At the same time, quantum computing is advancing toward a new era of computational power. As these technologies converge, they will significantly amplify one another’s capabilities, creating unprecedented opportunities as well as significant new security challenges.

This changing landscape requires such measures as deploying quantum-resistant security to safeguard data, AI models, and communications as quantum computers begin to challenge current cryptographic standards. Using AI can strengthen cybersecurity solutions through improved threat detection, accelerated incident response, and are resilient security architectures.

Learning Objectives:

• Learn how to counter the malicious use of AI by hostile state actors, extremist groups, and criminals in the face of quantum resources that accelerate model development and attack automation
• Identify how adversarial attacks on AI systems can exploit model weaknesses and ways quantum-enhanced methods may increase their sophistication
• Review your agency’s systems and datasets to locate and prioritize security vulnerabilities that need to be addressed

Public sector organizations are operating in an increasingly complex environment shaped by evolving cyber threats, aging infrastructure, workforce shortages and growing expectations for modern digital services.

AI-driven attacks are expanding the threat landscape while legacy systems and fragmented data make it harder for agencies and institutions to respond quickly. At the same time, constituents, staff and students expect faster, more intuitive services similar to what they experience in the private sector.

To keep pace, government and education organizations are modernizing IT operations, strengthening endpoint security and adopting automation and AI to improve both cybersecurity and service delivery.

The Ivanti Public Sector Summit brings together leaders from federal, defense, state and local government, education and industry to discuss practical strategies for securing endpoints, modernizing IT service management and delivering trusted digital services.

What you'll Learn

• Explain how modernization initiatives help agencies address evolving cybersecurity threats.
• Evaluate strategies for improving coordination between IT and security teams.
• Identify best practices for securing communications in classified and tactical environments.
• Describe key outcomes of effective vulnerability and patch management programs.
• Assess how AI and automation can improve IT service management and service delivery.

As agencies move to implement AI throughout their organizations, most are finding their AI efforts disrupted by fragmented, low-quality, or inaccessible data. This is a problem government shares with the private sector – a recent survey found that 79% of respondents said their AI initiatives are being hindered by limited access to data across environments.

These are not new problems for the government. There are still issues with data fragmentation and silos, poor data quality, complexities in applying governance and security requirements, and technical debt – legacy systems aren’t designed for modern analytics needs. And. of course, the pace of data generation continues to accelerate, adding to the pressure to clean and restructure massive numbers of datasets. That recent survey found that 60% of AI projects may be abandoned due to poor data readiness.

Learning Objectives:

• Outline the particular challenges in data readiness faced by your agency
• Delineate the steps to address those challenges, including prioritization and resource allocation
• Establish metrics to measure improvements in data quality so your agency datasets can be used by AI tools to produce trusted solutions

Join us as thought leaders from government and industry share their expertise, their experiences, and their suggestions for harnessing the power of AI to provide noticeable improvements to internal operations while improving security and opening up opportunities for new and expanded services to their constituencies. 

Agencies throughout state, local, territorial, and tribal governments are being encouraged to incorporate artificial intelligence (AI) into their everyday operations, to streamline processes, modernize outdated systems, and strengthen cybersecurity defenses. Using AI is viewed as one promising way to deal with shrinking budgets and increased demand for services by citizens. 

This three-day event addresses key issues that agencies must contend with in ensuring that investment in AI generates maximum benefit. 

Learning Objectives:

• Outline the capabilities of AI management and compliance platforms and match them to the needs of your agency
• Delineate the use of the platforms to maintain auditability and traceability
• Understand how these platforms can guard against “shadow AI,” unauthorized use of AI within the agency
• Identify available AI tools to determine which best fit the security needs of your agency
• Review ways to integrate AI cybersecurity into existing defenses
• Understand the nature and magnitude of the threats posed by AI-empowered attacks
• Delineate places in your agency’s IT systems where AI can serve as a bridge between legacy systems and new services for citizens
• Establish priorities for tasks and processes that can be streamlined through the use of AI tools
• Outline metrics that can measure improvements, such as improved accuracy in testing, cost savings through reductions in outside labor costs (such as coding), and faster turnaround time in updating apps

State and local agencies are exploring how to incorporate artificial intelligence (AI) into their operations – as long as they observe their own state-level and any applicable federal-level regulations.

To meet these requirements, AI governance and compliance platforms help organizations manage, monitor, and enforce policies for safe, ethical, and legal use of AI, covering the entire lifecycle from development to deployment, by automating risk assessment, bias detection, and access control. These platforms centralize control, provide transparency, track data lineage, and automate auditing to build trust and prevent issues like unfair outcomes or data breaches.

Learning Objectives:

• Outline the capabilities of AI management and compliance platforms and match them to the needs of your agency
• Delineate the use of the platforms to maintain auditability and traceability
• Understand how these platforms can guard against “shadow AI,” unauthorized use of AI within the agency

It is widely recognized that the introduction of AI tools is a two-edged sword when it comes to cybersecurity. Attackers, whether profit-driven hackers or hostile nation-states, are using AI to launch faster, wider-spread and more sophisticated attacks, including AI-generated phishing and spear phishing emails, malware capable of adapting to changes in defensive responses, and deepfakes that are very hard to detect.

State and local agencies are attractive targets, since there are many more of them and often do not have the financial or IT resources of federal agencies. 

This makes state and local agencies’ use of AI in cyber defense critical – AI tools can operate at machine speed and scale and adapt in response to evolving threats. These tools can significantly improve threat detection and intelligence by identifying anomalies and patterns signaling attacks under way; automating incident responses such as isolating compromised devices and resetting credentials; using Natural Language Processing (NLP) to flag sophisticated phishing and social engineering attacks; and prioritizing vulnerabilities to emphasize the most critical risks. 

Learning Objectives:

• Identify available AI tools to determine which best fit the security needs of your agency
• Review ways to integrate AI cybersecurity into existing defenses
• Understand the nature and magnitude of the threats posed by AI-empowered attacks

At its annual conference in October 2025, the National Association of State CIOs (NASCIO) focused on “measurable modernization” facilitated by artificial intelligence (AI). Intelligent, AI-powered tools can streamline IT, converting slow, manual legacy system upgrades into faster, cheaper, and more secure processes by automating code analysis, testing, data migration, and even generating code, ultimately making systems more efficient, scalable, and future-proof while freeing humans for higher-value tasks. 

There are several ways AI is suited to improve operations. To name just a few:

• By analyzing legacy code, such as COBOL, to understand its structure, find inefficiencies and even rewrite it into modern coding languages, AI reduces legacy debt and eases manpower needs for obsolete coding skills.
• AI tools can automate testing and quality controls, significantly speeding up quality assurance and moving apps to more easily supported operations.
• Using AI can generate predictive maintenance, identifying potential system failures and enabling self-healing capabilities.

Learning Objectives:

• Delineate places in your agency’s IT systems where AI can serve as a bridge between legacy systems and new services for citizens
• Establish priorities for tasks and processes that can be streamlined through the use of AI tools
• Outline metrics that can measure improvements, such as improved accuracy in testing, cost savings through reductions in outside labor costs (such as coding), and faster turnaround time in updating apps

• Understand the components and processes that comprise Open RAN systems
• Evaluate your agency’s systems and the groundwork that can be done now to prepare for open source hardware
• 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
• Delineate the investments your agency has planned and how they can adapt to 6G in future use
• Begin building a plan to harness 6G capabilities to enhance your agency’s performance and achievement of mission

AI introduces new vulnerabilities – such as data leakage, model manipulation, and uncontrolled access – even as agencies are still figuring out how existing risk and security frameworks apply. Recent news articles have reported that Anthropic’s newest AI model, Mythos, found 2,000 vulnerabilities in just seven weeks of testing commercially available software; Mozilla, for instance, reported Mythos identified 271 security vulnerabilities in Firefox 150. There have been instances where security teams are pulled in late and asked to “make it safe” after deployment decisions are already underway.

There are cybersecurity constructs in place that can help control access to AI tools and data. For example, the Zero Trust mandate already in place – “never trust, always verify” – strengthens requirements for access. Having an “identity-first” security structure can minimize the risks associated with AI adoption.

Learning Objectives:

• Identify existing cybersecurity weaknesses in existing processes, such as where security is being bypassed or bolted on too late
• Understand how to apply Zero Trust concepts to AI workflows
• Confirm cybersecurity alignment with guidance provided by the National Institute of Science and Technology (NIST) and the Cybersecurity and Infrastructure Security Agency (CISA)