Advancements in automation, machine learning and artificial intelligence are increasingly serving this purpose of “unburdening” analysts. With greater interest and adoption of these technologies into operations, there is more clarity of how they will work.

Leveraging these tools will allow staffers to get back to performing analysis rather than poring over the data itself. But in interviews with industry representatives, what’s becoming clearer is exactly how intelligence agencies can use these new technologies. In short: machines will pre-sort or manipulate data and then present its findings to a human for additional analysis, weeding out hours of manual labor.

Critical to this development is understanding the difference between artificial intelligence, automation and machine learning.

“I consider AI to be cognition, independent cognition. A true AI would be cognizant of itself and be able to create and create from nothing,” Rob Shaughnessy, senior manager at Deloitte, told C4ISRNET at the annual GEOINT symposium in Tampa, Florida. He noted that industry hasn’t seen this characteristic yet. With machine learning, “it can teach itself, but it’s teaching itself within a tightly defined lane … It’s being given boundaries to work in and learning within those boundaries.”

Shaughnessy added that machine learning is great for getting a machine to provide curated intelligence information to an analyst. It’s not so great at making decisions. AI, on the other hand, will make an absolute decision or the best decision.

However, “you don’t always want to make the best decision. There could be political reasons to not make what is the best logical decision. That’s for humans to do and that will always be for humans to do,” he said.

Some described the act of sifting through data to get to a decision as layered.

“We look at analytics as, after you have the data, what’s the intelligence you can get out of that data? Most of those analytics is how, with software, you can automatically produce some of these insights,” Jane Chappell, vice president of global intelligence solutions at Raytheon, told C4ISRNET at the same conference. “Machine learning, then, is just after you’ve done that and seen, ‘hey six times in a row when this happens, then that happens.’ That’s really the machine learning effort.

“Then AI ― which really in this industry we’re really not at an AI standpoint yet ― that’s just the code thinking on its own. We’re really not to the point right now. We’re [not] applying AI at any kind of scale in industry.”

After all, content or data is just that until it is turned into intelligence.

What can machine learning do for analysts?

“You can collect picture of ‘x’ for forever but if you’re not making sense out of any of it, you just have pictures of ‘x’ and that doesn’t help anybody,” Mike Manzo, director of intelligence, threat and analytic solutions at General Dynamics told C4ISRNET at GEOINT. “It’s content until we make sense of it. That sense could be made with a machine or that sense could be made with an analyst. Where it’s heading is it’s going to be made with both. Where the machine is really going through the monotony part and the analyst is really providing that values added piece and that’s really where things are going.”

Officials within the U.S. government have stressed that these new processes are not designed to eliminate humans, but to make them better at their job. And it’s imperative to leverage emerging technology to help make sense of the deluge of data in order to drive insights.

“Automated intelligence using machines, I call it that way because I’m not interested in the technology artificial intelligence, I’m interested in the outcomes we achieve,” Principal Deputy Director of National Intelligence Sue Gordon, said during a keynote presentation at the show.

Gordon praised the National Geospatial Intelligence Agency and its director for its aggressive adoption of these technologies. During the conference, NGA Director Robert Cardillo announced a robust goal of applying automation, artificial intelligence and augmentation to every image NGA ingests by the end of this year.

In its most simple form, leaders want these technologies to sift through the data and alert analysts of something significant.

“Specific to a customer, like here at GEOINT and NGA, analysts are poring through mounds and mounds of data. We need to get the analyst focused on the analysis. Looking at what’s important to determine what the best courses of actions are whether they’re national security or policy or the warfighter,” Damian DiPippa, senior vice president/general manager mission and intelligence solutions business unit at ManTech told C4ISRNET.

Manzo explained that the key is finding pinpoints from an analyst perspective having systems automated in the background to enable them to get to what they want to do, which is their job.

“What we’re hearing from them is I want to be an analyst, I don’t want to be a data gatherer, I don’t want to be a discoverer,” he said.

Manzo continued; “I want to know my data is pre-populated for me or is pre-stationed for me so when I get in in the morning I’m not spending two-thirds of my day hunting for the things I know I need to do my job … I found all my data, so that’s two-thirds of my day, now I have one-third of my day just to go through it all and by the time my day’s finished I haven’t actually gotten that far because I’ve spent most of it looking for and now discovering.”

Manzo described how a human, working through a machine, might work in practice through the lens of an analyst tasked with monitoring North Korean missile launches. The analyst knows in order for a launch to occur a clear space has to be found, a transporter erector launcher has to be present along with fuel trucks and maybe even people that aren’t normally in a particular area.

The machine can look at imagery data and when certain thresholds are met, alert the human. The human analyst can then take control back after receiving an alert, and using their critical thinking and expertise determining if in fact what is being observed is preparation for a launch or simply a training exercise.

Deloitte, similarly, displayed a machine learning capability that provided a great deal of insights from a video in a matter of seconds. The system was able to provide frame by frame analysis, object detection of objects in the video as well as concept detections such as power or time. The system can then provide timestamps of when these items happened in the video as well as a full timestamped transcript saving the analyst the time of watching the full video.

Additionally, Deloitte demoed what Shaughnessy described as one of the simplest examples to look at but one of the hardest things to do from a machine learning perspective. The machine was able to learn what an M1 Abrahams tank was based on a video of an Abrams spinning around. Moreover, the machine was able to begin to pick out unique identifiers of the tank, such as its tracks, and identify a different variant of the vehicle.

By having the machine identify tanks in pictures or full motion video, Shaughnessy said this makes a human’s job not just easier, but it changes it because the analysts can ask more complex and better questions.

Unburdening the analyst

A more startling example of problems technology can solve for analysts is observing behavior at a fixed location. Sometimes, analysts have to stare at a particular building or compound monitoring every vehicle and person that enters and track where they go when they leave, Josh Nauman, chief engineer at Harris Geospatial Solutions, told C4ISRNET. While the analyst is following a vehicle out of the compound, they have no idea what else is going in and out of that building, so then they have to go back to where they started and watch it again.

Harris is providing solutions that allow for the machine to monitor full motion video and imagery feeds to stare at the compound and track vehicles and persons going in and out alerting the analyst of all the instances. The system can also identify:

• Similarities, where there’s a known route of interest and the machine will alert the analyst any time a vehicle hits that route;
• Multiarea association, which monitors multiple locations at once and provides notifications anytime there is track activity between these locations;
  
• Meeting and clustering, which will monitor anomalous tracks, watch boxes and trip wires where an analyst is monitoring check points and notify them when there is movement in that activity.
  

Nauman said there’s a potential for new problems associated with greater adoption of machine learning, which is alert overload. Depending on how good the algorithms are, they might cue false alarms leading the analyst to investigate “false” alerts.

“We went from object extraction to now, ‘Are these extractions real, how do we validate them?,’ maybe a secondary analytic to validate the detections,” he said.

Another issue surrounds the accuracy of such algorithms. In order for the machine to learn objects, it has to have images on file to learn from. For example, Deloitte trained its system to identify a bus based upon 20 images from Google. However, in a military context, analysts might not have 20 images of the object they want the machine to identify or if they do, they might not be very high resolution.

Even more complicated is teaching the machine to identify a specific tank or bus.

“Something like ‘not a person’ or ‘not a car’ but ‘your car’ is more complex,” Shaughnessy said. “You have to identify what are the unique feature of your car.” This can include certain markings or dents that are unique identifiers to the specific object versus all similar objects.

But doing so is a complex change for agencies who regularly handle sensitive data and often work with a legacy system with massive infrastructure.

In the intelligence community, agencies are moving through a massive IT modernization effort, known colloquially as ICITE. One element of that modernization requires greater use of the commercial cloud, which can provide elastic, on-demand utility data and storage services.

To help make this transition easier, Noblis ESI, a wholly-owned subsidiary of the non-profit strategy organization Noblis, has compiled a guide to help federal organizations through the move. Noblis works with federal clients on solving technical problems.

“The concept of moving a portfolio to the cloud sounds wonderful to start out,” said Chuck Muggleworth, director of national ground infrastructure at Noblis ESI. “It provides a scalable infrastructure upon which one can build.”

Notably, the cloud allows for computing resources to help go from idea to prototype to development to operation. This is critical, especially for the defense and intelligence community, as threats continue to grow. “Our intelligence and defense communities need to keep up, not just with the commercial market, but with our adversaries,” Muggleworth said. “The threat is developing as fast as the commercial market.”

But moving to the cloud is not always a seamless activity. Several IT experts have noted that an organization’s migration to the cloud is almost always their first migration to the cloud, leaving room for improvement.

“The cloud has incredible capabilities. It’s not always the right answer,” Muggleworth said. “Understanding the mission need and choosing which technologies to integrate is more important. It has to be the right fit.”

In making the move to the cloud, agency IT leaders need to consider the business-side of the transition, which parts of the enterprise will move to the cloud, and which cloud (a public, private or hybrid cloud) those apps and portfolio could be housed on.

“That’s a much more interesting or challenging question than just wave our hands and say, ‘we’re going to the cloud,’” Muggleworth said.

IT leaders need to concentrate on security and application suitability, which includes the discussion of when, if and how they can migrate? Should they use docker containers? How processor intensive are the applications?

Think of a senior leader who wants to watch full-motion video of an area in the field. As more and more viewers come online, that scalability becomes crucial.

Muggleworth said the lessons learned can be particularly helpful if IT professionals read and absorb them before they start a transition, rather than once the process has already begun. Without following those, IT leaders risk running into classic project management problems. Among his tips:

– Organize the IT team and agency for success from the start.

– Realize the best solution may not use all the available tools out there. “It’s not like Pokémon,” he joked. “You don’t have to catch them all.”

– Don’t compete with other federal agencies. “We’re all on the same team,” he said.

– Finally, if a solution seems too hard or too expensive, it probably is.

This new dynamic world — one in which the truth can be much more easily fabricated — is posing problems for intelligence agencies that seek to provide policy makers with answers to questions and context surrounding world events.

“Facts are not what they used to be,” National Geospatial-Intelligence Agency Director Robert Cardillo said during a keynote speech April 23 at the annual GEOINT symposium in Tampa, Florida. “What’s real, what’s artificial, what’s fabricated and how do we know. In such a world, that’s less tethered to ground truth, one grasps for context and coherence.”

NGA will be releasing a new updated strategy soon, and while Cardillo told reporters at the symposium that the strategy will be mostly fine-tuning, one area of elevation from when the current strategy, now three years old, will be greater focus on adversaries’ capabilities.

“We have [to] … ensure that GEOINT is competitive with and can defeat in the race the adversary’s capability,” he said. “I probably was not as clear in [2015] because it wasn’t clear in my head about the nature of the adversarial use of common geospatial applications.”

One example Cardillo provided was the 2014 shoot down of a Malaysian passenger jet over Ukraine, attributed to Russian-backed separatists.

While there was debate and fact-finding efforts at the highest levels among western nations of who was responsible for the shoot down the Russians had two or three or four narratives out, Cardillo said.

“It was the Ukrainian military, it was a rouge operator who had stolen a piece of Russian equipment, it was a Ukrainian fighter plane,” he said of the Russian narratives. “There were three or four, you can’t even call them counter-narratives because they didn’t even have a central narrative yet. That’s my point.”

This was a great example, he added, of how adversaries can create confusion within the international community using something as simple as a doctored image.

“That was five years ago, it was done with Adobe photoshop. It was pretty simplistic. The world’s gotten a lot better. I don’t know that it will be as obvious next time,” he said. “Somebody has a picture that looks really authentic that supports your counter-narrative, boy that’s confusing.”

Cardillo said there is already similar counter-narratives occurring in Syria following the multilateral April 13 strikes against alleged chemical weapons factories.

While these practices are hardly new, Cardillo expressed that with the advancements in technology today “people are going to get a lot better at it and, let’s face it, in a world in which we tend to live in our own news cocoon, we know what we like … it’s really easy to reinforce somebody’s internal narrative with a doctored picture.”

Robert Cardillo, the organization’s head, announced the new strategy during his keynote speech April 23 at the annual GEOINT symposium in Tampa, Florida. Cardillo told reporters following the announcement that at first he didn’t immediately want a new strategy given the existing one, just three years old, was pretty good.

However, given the release of the recent White House National Security Strategy and the Department of Defense’s National Defense Strategy, he thought he should refresh and fine-tune NGA’s core documents as well.

The new strategy will be aligned across four goals:

• Inspire and grow the GEOINT workforce;
• Fuel and drive the global GEOINT enterprise;
  
• Secure and deliver the most trusted GEOINT, and;
  
• Bring the future into focus to anticipate the opportunities and foresee threats.
  

“In a world where knowns are elusive and confidence is fleeting, GEOINT must be instantly available, expertly validated, highly trusted and easily consumed,” he said. “My message back to my workforce is this is not a brand new strategy, because it isn’t. It is a refinement of what we were doing before.”

Notably, they said, GEOINT has helped provide partner nations confidence in the targeting process against the Islamic State group.

Over 50 nations contribute to this global anti-ISIS coalition. When targets are validated and nations bid to drop bombs on them, there must be intelligence information that each nation can view before agreeing to participate in that strike, said Maj. Gen. Karen Gibson, the director of intelligence at Central Command.

If U.S.-only human intelligence or Five Eyes-only signals intelligence is the basis for the decision, “the Danes or the French or the Italians are not going to drop bombs on” a compound, she said.

“GEOINT was often a deciding factor in the defeat ISIS campaign for getting our coalition partners on board with conducting kinetic strikes,” Gibson added.

There is now a recognition at the highest levels of leadership that conflicts will be fought with multilateral partners, panelists said.

“I was in a meeting a couple of months ago with Gen. [Joseph] Dunford and it was surprising to hear, we were sitting with many four stars, he said quote, ‘It’s no longer a bi-lateral world, we will no longer fight with bi-lats, we will fight with multi-lats,’” Maj. Gen. Linda Urrutia-Varhall, associate director for operations at the National Geospatial-Intelligence Agency, said during the same panel. “That’s the first time I ever heard that in the Pentagon with the other four stars.”

“Think about the overwatch we provide to make sure that penetrating fighter or a bomber can get in. Now we have to scale that to case officers,” Dawn Meyerriecks, deputy director of the CIA’s directorate of science and technology said during an April 22 keynote at the annual GEOINT conference in Tampa, Florida.

This change revolves around what Meyerriecks described as identity intelligence, which means the pervasiveness of closed circuit television coverage, wireless devices and interconnectivity globally makes operating clandestinely extremely difficult.

“If you have supposedly a six figure or low seven figure income, and you own no real estate, you don’t have any health insurance, like big life insurance policies to speak of, and you turn your cell phone off every day from 8 to 5, who do you work for,” Meyerriecks said.

When combining technologies like smart cities, industrial internet of things, the advertising markets, “what does that say about our ability to operate,” she asked.

To help cope with the seemingly easier ability to be outed, Meyerriecks said folks are going to have to live their cover in a whole different way.

“When I’m watching a movie with my family in a theater, I might be connected but I’m not responding. If I’m sitting in a dentist chair, I might be connected but I’m not responding,” she said. “There will be those opportunities where you want to see a movie with your family, or at least that’s what all your digital dust indicate, but that might not be where you physically are. But it will look like your normal pattern of life because in fact it is your normal pattern of life.”

But these same patterns, especially digitally, could be interesting to salespeople as well.

While not the end of human intelligence as previously known, Meyerriecks said understanding how one’s pattern of life looks to an advertising agency or a smart city will be a sea change.

“If you think about what it takes to do a handful of platforms today, think about scaling that to the officers that actually do things like HUMINT or need to be able to travel clandestinely in the future,” she said adding, “everybody’s going to need that.”

While a CTO is a new position at NGA and not the federal government writ large, the role and placement within NGA hierarchy is different, the new CTO Anthony Vinci told C4ISRNET in an interview.

The CTO reports directly to the director and the deputy director as opposed to the chief information officer.

NGA has been a robust adopter of automation and technology within the government in order to help reduce the burden on its analysts, but a variety of culture and policy hurdles inside and outside the government persist.

Vinci serves a few primary roles with his position: chief technology adviser to the director and deputy director to help them understand technology trends in the commercial sector; chief evangelist who champions technology inside and outside the agency; and tackling technology transformation in the agency that might require cross cutting approaches to implement and adopt.

Vinci said he is pushing for greater adoption of artificial intelligence and automation.

“There’s just no possible ways that human beings alone can ingest, digest analyze all the information that are at our fingertips right now so we have to adopt AI and automation technologies,” he said, adding there are substantial cultural issues related to such widespread adoption.

“There are workforce issues that have to be addressed, training, recruitment, new work flows, new processes in order to take advantage of those technologies, it’s not just adding an icon to a desktop,” he said. “It’s a fundamentally new way to work with technology when you start using … machine learning.”

The agency itself has set a goal to automate 75 percent of functions within five years, which will allow analysts to focus on things like anticipatory analysis without being bogged down with too much.

Vinci explained he went on a “listening tour” to understand what the operational community needs. By understanding what the operators need and what commercial technologies exist today, he can go back to the agency and better understand out how to deliver them.

One of the other big tasks Vinci is undertaking is spearheading public-private partnerships and trying to work with commercial industry in new and different ways.

While NGA has worked with commercial industry for many years, what has changed is the need to partner with nontraditional partners and technology firms in Silicon Valley. But to do that requires a different approach, he said.

The technology sphere moves extremely fast and the current procurement cycle does not always facilitate that. For example, Vinci said an algorithm that could be valuable might be useless in six weeks because it was overtaken by something better.

“To move that quickly to adopt technology that quickly not just to try and prototype but actually to put it into production and use for operational purposes demands that you have to be able to move that quickly and go from contracting to test and evaluation to usage in an operation setting in a matter of weeks, not days,” he said.

“To do that, again like I was saying, you need to bring together a lot of pieces in a way that we traditionally haven’t always done where we could take months or even years of time to adopt technology.”

One way he’s helping to foster and instill these nontraditional practices is setting up some bureaucracy, something that many think is the antithesis of the Silicon Valley model.

“It seems strange, but we actually need some bureaucracy and process around adoption and around incubation of technology development of demos and prototypes and then turning those demos and prototypes … into operational capabilities,” Vinci said.

NGA has created an Office of Ventures and Innovation to help set standards and processes to take new commercial technology through government development for test, evaluation and funding, eventually putting it through workflows and standard practice.

What emerged following a pair of hearings in the House and Senate and hours of testimony is a laundry list of terror and a detailed accounting of exactly what keeps military leaders up at night. This year’s presentations also revealed a strategic shift as the Pentagon and intelligence agencies prioritize threats from China and Russia over terrorism for the first time since 2001.

“We will continue to prosecute the campaign against terrorists that we are engaged in today, but great power competition, not terrorism, is now the primary focus of U.S. national security,” Jim Mattis, the secretary of defense, said in January 2018 while unveiling a new national defense strategy.

For many years, machine learning and artificial intelligence have been held up as one answer to preserving U.S. military superiority. But now, with other nations making significant investments in that technology, it’s easier for peers to make sense of the copious data and sensors in the field. With that benefit, nation states can shrink the decision space and create actionable decisions faster than before.

“What’s changed is the world around us and now within us,” Robert Cardillo, director of the National Geospatial-Intelligence Agency, told the Senate Intelligence Committee during a Feb. 13 hearing. “What we used to hold exclusively — because we had capabilities that others didn’t — is now more shared.”

What became clear from this year’s hearings is that military leaders are now universally holding up artificial intelligence as a threat to U.S. military operations.

Because many of the tools in the computing world that make analysis possible are available commercially — and thus available to all — operational concepts will be key as these modern capabilities become available worldwide, said Lt. Gen. Robert Ashley, director of the Defense Intelligence Agency, during the same hearing.

Adversaries taking advantage

National security officials have listed China and Russia as top threats, indicating they have made tremendous technological

progress. China, for example, has a national strategy designed to harness the power of artificial intelligence. These experts say China is using AI in offensive and defensive cyber operations, as well as for intelligence, surveillance and reconnaissance. Russia is also experimenting with AI for drone swarms and electronic warfare.

“We’re seeing all of our near-peer competitors invest in these kinds of technologies because it’s going to get them to decision cycles faster, allow them to digest information in greater volumes and have a better situational awareness of what’s happening in the battlespace” and in some cases what’s happening in the strategic environment, Ashley said.

Adm. Michael Rogers, director of the National Security Agency, told the Intelligence Committee that five or 10 years ago he’d look at data sets and think they were so large there was no way an adversary could generate insights from them. Those days are over.

“I don’t have those kinds of conversations anymore,” he said. “With the power of machine learning, artificial intelligence and big-data analytics, data concentrations now increasingly are targets of attraction to a whole host of actors. You have watched [China] and others engage in activity designed to access these massive data concentrations.”

Former Deputy Secretary of Defense Bob Work has warned that potential competitors have reached parity with the United States in battle networks. These networks can include sensor grids that look at what is happening in theater, intelligence grids that make sense of what’s happening and determining desired effects, effects grids to make those goals happen, and logistics and support grids that keep the whole operation running.

Iron Man, not Terminator

Increasing manpower is no longer the answer to these military problems.

“We’re not going to industrial age out of this. ‘Well, it’s just hire 10,000 more people.’ That’s not a sustainable strategy,” Rogers told the Senate Armed Services Committee during a Feb. 27 hearing. “Among the things we’re looking at — and we’re not the only ones — is how can you apply technology to overcome the human capital piece.”

Technological solutions must augment, not replace humans. Work has commonly described this as the Iron Man, not Terminator, solution. “I tend to think of it more as Iron Man where it’s human-machine collaboration — where artificial intelligence helps Tony Stark make decisions and augments his capabilities,” Work said in a January podcast.

Perhaps the best example of this is using machines to ferret out meaningful intelligence in gigantic data sets. Information is power, except when there is so much to sift through it is nearly impossible to do so and make actionable decisions in any timely manner.

“We are now at the point for the first time I can remember where [processing, exploitation and dissemination] is the shortfall more so than the platforms themselves,” Lt. Gen. Jack Shanahan, director of defense intelligence for war-fighter support, said during an event in March 2017.

Machines can analyze data sets and video feeds in nanoseconds and point out critical elements for humans. Staffers can then spend their time on analysis of intelligence to enhance and inform decision making on the battlefield.

“Existing machine-learning technology, for example, could enable high degrees of automation in labor-intensive activities such as satellite imagery analysis and cyber defense,” according to the intelligence community’s 2018 Worldwide Threat Assessment.

Cardillo has been aggressive in pursuing automation and machine-learning technologies to help unburden analysts. He described during a 2016 congressional hearing his frustration with human-machine teaming at NGA, providing an example in which an analyst physically counted 25,000 buildings in a particular area as there was no algorithm designed to do this for them. “Think of the hours he had to spend to do that,” Cardillo exclaimed. “I turned to my head of research and I said: ‘Don’t let that happen again.’ ”

NGA, as a result, has been trying to get to 75 percent automation and 25 percent analytical to allow analysts to tackle hard problem sets and provide anticipatory intelligence.

How is DoD solving the problem?

The White House’s budget request for fiscal 2019 makes clear the Pentagon is looking to spend on several programs related to AI, machine learning and automation as a means of increasing decision speed.

Line items from the Pentagon’s FY19 budget request point to funding asks in a wide array of AI applications. These include training and war gaming, combat systems, and robotics.

A few examples include an $87 million request for Air Force experimentation to put operational AI in simulations and field experiments; a $49 million ask for a Navy prototype development program to focus on innovative combat system technologies; a $7.1 million ask for a Marine Corps program to develop drone swarming technology that can fuse AI to enhance situational awareness; and an Army program seeking $4.6 million to improve robots’ perception of their environments.

Other programs remain underway. On a conceptual level, Work created the so-called Third Offset strategy, which sought to counter the advancements in recent years of competitors’ theaterwide C4I grids and leveraging human-machine teaming through artificial intelligence.

Current officials have acknowledged that the notion behind the Third Offset lives despite no direct or public reference to the moniker. Another government program, Project Maven, aims to accelerate the Department of Defense’s integration of big data and machine learning, first focusing on processing full-motion video in Iraq and Syria. As top adversaries invest in these capabilities, thereby increasing the pace of decision-making during wartime operations, leaders are adamant the U.S. must take this seriously.

“From a military perspective, my concern is you potentially lose speed and knowledge. That’s a terrible combination as a warrior,” Rogers told the Senate Armed Services Committee in response to a question asking what he fears if the U.S. doesn’t get this right.

“Speed and knowledge are advantages for us historically,” he then added. “One of my concerns is if we’re not careful, AI potentially gives opponents speed and knowledge better than ours if we’re not careful. I’m not arguing that’s going to happen but I acknowledge we’ve got to look at it.”

Markham will take over at agency’s west branch in St. Louis, assisting with the integration of NGA’s activities across the western United States. According to an NGA press release, Markham will oversee the NGA Operations Center, the Office of NGA Defense, the Office of Expeditionary Operations, an NGA leadership at the three National Reconnaissance Office Aerospace Data facilities.

“I know [dual-hatting] is a departure from past practices, but I strongly believe it reflects how the position and our leadership structure has evolved,” Cardillo said. “[Brett] knows the mission, the workforce and the community.”

Prior to this appointment, Markham served as the agency’s director of analytic operations in the analysis directorate. Over the course of his 28 year naval career Markham served in various analytic, staff and leadership positions that included five strategic deterrent patrols, three carrier deployments, and service at three Combatant Commands and the Joint Staff. Markham also served as the deputy director of intelligence for North American Aerospace Defense Command and the United States Northern command.