Today’s intelligence community has both the good fortune and the challenge of having massive amounts of data at its fingertips.

There are more systems and applications available to gather, store, and analyze it – that’s the good part.

There is more data being collected today than any organization can effectively process without advanced data solutions – that’s the challenge.

And there are more of these advanced analytic capabilities, like AI and machine learning, available than ever – that’s both.

So how can intelligence agencies navigate this reality?

From our perspective, it comes down to building resilient, distributed infrastructures; adopting a service-centric architecture approach; and enabling AI at scale across the enterprise.

Building a Resilient Foundation

Right now, there is so much intelligence data being collected – from satellites, emerging sensors, communications, current events and more – that no one location or system can house it all. Across all industries and sectors, data is being collected at a scale we’ve never seen before. For the intelligence community, enabling resilience and data consumption at scale requires modular, interoperable, distributed, and open systems so new capabilities can be easily integrated and reused globally. This is being achieved using advanced technologies such as cloud computing, microservices, API’s, artificial intelligence, and machine learning.

Further, leveraging software-defined networking and mesh computing principles, data can be distributed and accessed globally to support, what is dubbed, disrupted, disconnected, intermittent and low-bandwidth (DDIL) environments. With no single points of failure, resilience is built into the system and continuity of operations is preserved. This is critical for next-generation intelligence collection, storage, and analysis.

Adopting a Service Centric Architecture

Another important element of today’s data reality: shifting to a service centric architecture. In the past, mission partners built fixed, prescribed solutions on-demand. Today we are focused on the delivery of modular and loosely-coupled services across the enterprise, built on virtualized infrastructure layers, rather than through dedicated hardware or software components. This shift has led to increased flexibility and agility in data operations, as well as cost savings and scalability, when paired with effective service management and data governance. Additionally, to meet the global data needs of the intelligence community, it is necessary to move beyond relying solely on cloud capabilities or isolated data centers. Instead, a more comprehensive approach is required, one that incorporates robust hybrid-cloud, service- centric, and edge-based DDIL capabilities. This ensures that data can meet the specific mission requirements of the intelligence community in any environment.

Enabling AI at Scale Across the Enterprise

There are two sides to the AI coin. First, analysts need the ability to use AI models and algorithms to make predictions, detections, and classifications to process the vast amounts of data being collected. Second, it is essential to build the underlying capabilities to make AI scale. Although a great machine learning model is valuable, it is significantly less helpful without the capacity to inference and re-train on a large scale or share those outputs across the enterprise. Therefore, intelligence community analysts and mission partners need to build with the understanding that data must move seamlessly between agencies, partners, platforms, tools, and models. Teams require machine learning pipelines and platforms to fully leverage AI's potential and bring its capabilities to the entire enterprise. Consequently, enterprise AI is emerging as a critical component in enabling AI across the intelligence community. GDIT provides enterprise AI solutions that help intelligence community analysts and mission partners fully exploit the benefits of AI technology.

Undoubtedly, building resilient and distributed infrastructures, adopting a service-centric approach, and enabling AI at scale will significantly improve the intelligence community's ability to manage the ever-increasing volume of data. We look forward to continuing to support our intelligence community customers on this important and essential work.