How Multi-INT Fusion Accelerates Mission Intelligence for Real-Time Decision Advantage
Intelligence analysts are drowning in data amid ever-increasing numbers of communication channels, devices, and open-source intelligence.
Intelligence professionals often apply an adaptation of the Pareto Principle (aka the 80/20 rule) to the challenges created by all this data. Analysts anecdotally spend 80 percent of their time looking for data and information, leaving only 20 percent to analyze, render judgments, and articulate their analyses in a way that’s useful for decision makers. In this age of fast-evolving threats, it’s time to flip that number.
For the intelligence community (IC), access to near real-time information and actionable analysis is mission critical. That’s where multi-INT fusion comes in. Multi-INT fusion is the fusing of multiple data types to convert data to information more easily, enabling analysts to understand context and render intelligence assessments faster. It also involves converting manual tradecraft to more automated processes, empowering analysts to accelerate and improve their analyses and gain insights more quickly.
While multi-INT fusion is already a long-established practice, intelligence agencies can now apply new and emerging technologies to achieve it in faster, more efficient ways, in turn freeing analysts’ time for the problems that require more critical thinking. Here are two things IC leaders should consider as we drive toward a large-scale revolution in multi-INT fusion.
Thinking Big, Starting Small
Advancing multi-INT fusion requires a multi-prong approach. Not everything can change overnight, but the IC can seek quick wins that work with legacy systems, while remaining on the path toward enterprise transformation. For legacy systems, automation tools can be as simple as recommenders or real-time bidding – the same idea as seeing suggestions in a Google search or on your Waze app. Based on the user’s search patterns or location, this type of automation can often narrow down (and accelerate) analysts’ research efforts.
Ultimately, algorithms like those used in recommenders refine results based on what an analyst is looking for or is interested in, thus creating significant time savings. Similarly, we recently worked with a partner in the IC to develop an automated source extractor capability, which reduced the time it takes an analyst to properly cite sources by 75 percent. Not only do micro applications like this save analysts time, but they can also be rapidly integrated into legacy systems because they are low-cost and don’t take up much bandwidth.
More sweeping transformation might look like integrating advanced capabilities such as artificial intelligence (AI) and machine learning (ML) into new systems from the get-go. AI/ML can draw connections much more quickly than a human analyst can, which even more advanced AI can then analyze to predict outcomes and recommend action. In turn, AI/ML can correlate target activities to create faster decisions. This isn’t replacing the role of the human analyst, but rather increasing the pace of analytics to machine speed. This is also achieved by integrating AI/ML with advanced data science techniques, such as probabilistic and predictive modeling and automation of data pipelines.
Understanding Where Open Architecture Can Help
As intelligence organizations modernize, it’s important to create more sophisticated cross-domain pipelines. Fusing new, classified streams with commercially available and open-source data at scale requires layered levels of data engineering and analysis.
The data must be indexed, fused, and analyzed at appropriate security levels for multiple stakeholders, not just in the IC but also military, government, and international partners. Then, those recommendations need to be distilled to send to leaders at their level of access, whenever needed. Such large-scale, diverse fusion requires open architectures that move operations to the cloud for bandwidth and scale. To work within allotted budgets, many intelligence agencies should approach large-scale modernization incrementally, making small changes that build toward a larger vision.
Taking a modular open architecture approach simplifies transformation, while also allowing agencies to make decisions according to their funding resources. Often the answer lies in tools and frameworks created from commercial off-the-shelf or open-source software – ideally, tools that are already cleared through the accreditation process, have intelligence-level cybersecurity built in, and are proven in operation. The right technologies and solutions accelerate decision-making while providing capacity, all working together seamlessly, for faster, smarter analysis.
By Rob Goodson and Saurin Shah, Vice Presidents in Booz Allen Hamilton’s national security business