Presented by Aviz Networks & Carahsoft
Intelligence organizations have no shortage of tools, including legacy capabilities built over many years. Thomas Scheibe, Chief Product Officer at Aviz Networks, says the opportunity is to use artificial intelligence and existing data to help those organizations make better decisions faster—and move some decisions closer to where the action occurs.
The urgency is growing because AI changes the threat environment as well as the mission environment. Models can help discover vulnerabilities more quickly, which means organizations may need to assess and update systems continuously rather than on a weekly or monthly rhythm. Waiting weeks for a decision can leave an avoidable window of risk.
Scheibe points to network-copilot capabilities as one response. Such a tool can examine an organization’s installed base, bring together notifications and risk factors, and recommend action. The goal is to give a responsible human enough context to make a decision daily and closer to the operational edge.
The quality of that information depends on data. Organizations sometimes approach AI as if it can make incomplete, inconsistent or inaccessible information disappear. Scheibe rejects that premise. Agencies should begin by understanding where their data sits, whether access is consistent, where sources overlap and what needs to be cleaned up.
Data fidelity helps an AI-enabled capability produce grounded, deterministic decisions rather than hallucinations. This does not require inventing a completely new data estate. Proven existing information can remain valuable if the organization rationalizes it and makes it usable across the workflow.
Legacy modernization should follow the same principle. Scheibe does not equate modernization with throwing away existing systems and their data. In many cases, the central obstacle is that datasets reside in different branches or technical silos that do not communicate effectively.
An organization can modernize the way it accesses those sources and place an AI-enabled layer above them to stitch information together. That approach preserves investments while enabling a more complete view. Decision-makers gain access to related information without waiting for an enterprise-wide replacement effort.
Connecting silos does not make governance less important. Agencies still need to define who may access each source and under what conditions. User-based controls can be maintained even when agents assist employees or retrieve information across systems. Rules allow data to move across organizational boundaries without turning access into a free-for-all.
This balance is central to responsible AI adoption. The technology should reduce the time between a risk signal and a decision, but a human remains accountable. Data should become more accessible, but permissions remain enforceable. Legacy information should be reused, but its quality and overlap must be understood.
For intelligence organizations, success is an AI-enabled environment grounded in what they already know. By improving data fidelity, connecting silos and giving people actionable recommendations, agencies can reach better decisions faster without pretending that AI replaces governance, expertise or human judgment.