When security professionals think of information warfare, most think of offensive attacks that occur in cyberspace. These attempts by potential adversaries to exploit the digital battlespace range from denial-of-service attacks on networks and inserting malicious code into government and industry systems, to burrowing into government networks to steal information and manipulating social media to influence elections and other events.
But while these information warfare threats are serious, there is another issue that is potentially more vital and that cries out for increased attention from security and defense officials: The ability of the U.S. military to use information is not keeping pace with the vast amounts of information it collects—at great effort and expense. Most agree that no amount of effort by even the most motivated humans will enable them to curate and exploit the Department of Defense’s (DoD’s) data reservoir. We can only begin to make effective use of the data military platforms collect by fully leveraging artificial intelligence (AI) and machine learning. And for a variety of reasons, the U.S. Navy should lead this effort.
Not Just a Navy Challenge, But . . .
All military services have sought to use gaining or denying information as a weapon—witness the Union Army’s success in using the telegraph to coordinate the actions of its far-flung armies during the Civil War, Great Britain’s breaking of the German Enigma code, or the U.S. Navy’s Combat Intelligence Unit cracking the Japanese Navy’s general operational code. Gaining the information advantage has changed the course of battles and history.
Dramatic code-breaking operations, however, represent only a fraction of the larger discipline of information warfare. Acquiring, moving, and using information is the lifeblood of effective decision-making. And because naval operations span the globe, it is navies that have been most active in collecting, curating, and using data.
In 1904, Britain’s First Sea Lord, Admiral John Fisher, developed what Norman Friedman dubbed “picture-based” warfare. Fisher used information gleaned from shipping reports and dispatches from his own fleets to build a tactical picture of where pirates were attacking British merchant ships. This information was fed into two war rooms. The first tracked ship movements around the world, while the second tracked ship movements in the North Sea. Armed with this picture-based view of the world, Fisher was able to direct warships to the spots where British merchants were at risk.1
As technology evolved, so did the ability of navies to use this new concept of “networking” to achieve decisive results. In the 1960s, the U.S. Navy deployed the first computerized information processing systems: the Naval Tactical Data System (NTDS). NTDS took reports from multiple sensors on different ships and collated them to produce a single unified map of the battlespace. This technology evolved in the 1990s to the Copernicus C4I initiative, designed to create a common tactical picture.2
Later that decade, Vice Admiral Arthur Cebrowski and John Garstka built on Copernicus and the lessons of Desert Storm to envision warfighting in the 21st century. Their 1998 Proceedings article, “Network-centric Warfare: Its Origin and Future,” described the potential of network-centric concepts to alter the nature of warfare itself. Although the article was published more than two decades ago, its vision of network-centric warfare proved remarkably prescient:
Network-centric Warfare derives its power from the networking of a well-informed but geographically dispersed force. The enabling elements are a high-performance information grid, access to all appropriate information sources, weapons reach and maneuver with precision and speed of response, value-adding command and control (C2) processes . . . and integrated sensor grids closely coupled with shooters and C2 processes.3
Under the Navy’s “Sea Power 21” initiative, unveiled in 2002, Copernicus and the concept of network-centric warfare evolved into FORCEnet, which was described as the glue holding together the other components of “Sea Power 21”: Sea Strike, Sea Shield, and Sea Basing.4 In much the same way that the internet enabled the sharing of information globally, FORCEnet was essential to the Navy’s ability to control and coordinate its far-flung operations.
Proponents of FORCEnet and its predecessor networking methodologies saw their theories vindicated during Operation Enduring Freedom in Afghanistan. As then-Chief of Naval Operations Admiral Vern Clark observed regarding the Navy’s experience in Enduring Freedom, “Eighty percent of the Navy strike sorties attacked targets that were unknown to the aircrews when they left the carriers. They relied upon networked sensors and joint communications to swiftly respond to targets of opportunity.”5
Not Enough Human Bandwidth
The Navy has invested heavily in both manned and unmanned platforms that can range across the oceans and collect an enormous amount of data. This is good as far as it goes; however, humans cannot, unaided, deal with more than a tiny fraction of the data that is collected. The limits of what a human alone can accomplish in the information warfare sphere have been addressed by a number of observers.
As Dr. Alexander Kott, chief scientist at the U.S. Army Research Laboratory, said at a command-and-control conference, “The human cognitive bandwidth will emerge as the most severe constraint on the battlefield.”6A U.S. Air Force Technology Horizons report described the challenge this way: “Although humans today remain more capable than machines for many tasks, natural human capacities are becoming increasingly mismatched to the enormous data volumes, processing capabilities, and decision speeds that technologies offer or demand.”7
More recently, Dr. James Mancillas, science advisor for the Army Futures Command, noted that “the principal feature of information age warfare—the ability to gather and store communication data—has begun to exceed human processing capabilities. Future AI systems offer the potential to continue maximizing the advantages of information superiority, while overcoming limits in human cognitive abilities.”8
Sadly, the current concept of operations for dealing with the data collected by the Navy’s intelligence, surveillance, and reconnaissance (ISR) platforms depends almost entirely on manual human effort. A decade ago, it was estimated the U.S. Navy would soon face a “tipping point,” after which it no longer would be able to process the amount of data that it was compiling.9 The Navy long ago exceeded that threshold.
A Navy Use Case: Triton
To exploit technologies such as artificial intelligence and machine learning to help it deal with its current data deluge, the Navy must understand what information its commanders need to gain an advantage over an adversary. Whether it is Captain Isaac Hull seeking to take the USS Constitution into action against HMS Guerriere in August 1812, or a carrier strike group commander in 2021 considering taking his ships into a potentially contested area such as the South China Sea, a commander at sea needs to:
Know what is ahead of the force.
A strike group commander has many assets that can look ahead of the force to assess the tactical situation. He may use an MQ-4C Triton unmanned aerial vehicle (UAV) to perform this scouting mission. Today, a Triton operator receives streaming video of what the platform sees. But this requires him to stare at this video for hours on end (the Triton has a 30-hour endurance), seeing mainly empty ocean spaces.10
Using AI and machine learning, the MQ-4C can curate the data it collects and send only the information the commander will find useful. It can be trained to send only video of each ship it encounters, thereby greatly compressing human workload. Taken to the next level, the Triton could do onboard analysis of each contact to flag it for possible interest. For example, if a vessel is operating in a shipping lane, has filed a journey plan with maritime authorities, and is providing an Automatic Identification System (AIS) signal, it likely is worthy of only passing attention by the operator, and the Triton will flag it accordingly. If, however, the vessel does not meet these criteria (for example, it makes an abrupt course change that takes it outside shipping channels or has no AIS signal), the operator would be alerted. As this technology continues to evolve, a Triton—or other UAV—could be equipped with classification algorithms that have the potential to lead to automatic target recognition.
Have that information communicated back to the flagship.
Once the Triton has processed this information, AI and machine learning can help determine how to communicate with the flagship. In today’s contested electronic warfare environment, different communications paths have varying levels of vulnerability. Prior to Triton’s launch, the commander can determine the acceptable level of risk of communications intercept, as well as the risk of giving away the presence of the strike group.
Armed with this commander’s intent, and using AI and machine learning, the Triton can assess the electronic environment, select from multiple communications paths, and determine which path offers the least vulnerability of intercept.11 If the Triton determines the vulnerability is too high, it can fly back toward the flagship and communicate via line-of-sight UHF. Given the size and growth potential of the Triton, it could even carry a smaller UAV and launch it back to the force to deliver this surveillance information.
Make an informed decision.
Onboard the flagship, the commander must make sense of the curated information his sensors have collected and then make a number of time-critical decisions. Should he continue forward, wait, or retreat? Should he scout ahead, or in a different direction? Should he call on other forces, or are his organic assets sufficient to complete the mission without undue risk to his strike group? This is where AI and machine learning can make important contributions.
Should the commander choose to forge ahead and force an engagement, AI and machine learning can do what today’s rudimentary tactical decision aids cannot—offer a range of options and assess the pros and cons of each. Importantly, these technologies do not—and should not—make the decision, but rather should provide the commander with sufficient, well-curated information so he can make the best decision faster than the adversary can react.
This is a relatively simple example of how the Navy might better exploit platforms that gather data to give commanders access to useful information so they can derive a warfighting advantage over an adversary. However, it is emblematic of what the Navy must do if it wants to fully leverage the enormous advantages its collection platforms in all domains—air, sea, undersea, space, and cyberspace—can provide the naval commander.
Implications for Information Warfare
In his congressional testimony, responding to a question regarding his top priority for DoD technology modernization, former Secretary of Defense Mark Esper said:
For me it’s artificial intelligence. I think artificial intelligence will likely change the character of warfare, and I believe whoever masters it first will dominate on the battlefield for many, many, many years. It is a fundamental game changer. We have to get there first.12
It is increasingly clear that the United States, and especially the U.S. military, must outpace its peer competitors in leveraging AI and machine learning. In its 2019 interim report, the National Security Commission on Artificial Intelligence was unequivocal in its analysis of how AI will be a game-changer, noting, “AI will shape the future of power.”13 In 2020, The Future of Defense Task Force Report put the imperative to insert AI and machine learning into U.S. military weapons systems this way:
Whichever nation triumphs in the AI race will hold a critical, and perhaps insurmountable, military and economic advantage. . . . The advent of algorithmic warfare, where AI-enabled weaponry driven by speed and precision compete in a complex battlespace, requires the United States to invest significantly in both offensive and defensive AI capabilities.14
For the U.S. Navy, which must operate across the globe in an increasingly antiaccess/area denied environment, ensuring that its commanders can not only “guess what’s on the other side of the hill,” but also have precise knowledge of the disposition of friendly, enemy, and neutral forces is arguably the most important component of today’s information warfare continuum. It is long past time to for the Navy to invest more heavily in artificial intelligence and machine learning capabilities to fully leverage the data deluge its ISR platforms collect.
1. Norman Friedman, “Netting and Navies: Achieving a Balance,” paper presented at the Royal Australian Navy Sea Power Conference, Sydney, Australia, February 2006.
2. Loren Thompson, Networking the Navy: A Model for Modern Warfare (Arlington, VA: Lexington Institute, 2003).
3. VADM Arthur Cebrowski, USN, and John Garstka, “Network-centric Warfare: Its Origin and Future,” U.S. Naval Institute Proceedings 124, no. 1 (January 1998).
4. ADM Vern Clark, USN, “Sea Power 21: Projecting Decisive Joint Capabilities,” U.S. Naval Institute Proceedings 128, no. 10 (October 2002).
5. Thompson, Networking the Navy.
6. Keynote Address, 22nd Command and Control Research and Technology Symposium, Los Angeles, CA, 7 November 2017.
8. U.S. Army Training and Doctrine Command, “Mad Scientist” Blog, 23 November 2020.
9. The ISR “tipping point” has been noted in a tasking, collection, processing, exploitation, and dissemination (TCPED) study by the Office of the Chief of Naval Operations and the Battlespace Awareness and Information Operations Program Office, an independent Navy Cyber Forces study, and the NRAC study from summer 2010.
10. See Gabe Harris, Cynthia Lamb, and Jerry Lamb, “Surf the Data Tsunami,” U.S. Naval Institute Proceedings 144, no. 2 (February 2018).
11. See Jonathan Vandervelde, “Disrupt the Spectrum with AI,” U.S. Naval Institute Proceedings 143, no. 5 (May 2017), and Connor McLemore and Hans Lauzen, “The Dawn of Artificial Intelligence in Naval Warfare,” War on the Rocks, 12 June 2018.
12. Testimony of U.S. Secretary of Defense nominee Mark Esper before the Committee on Armed Services, U.S. Senate, 16 July 2019.
13. National Security Commission on Artificial Intelligence, Interim Report, November 2019.
14. House Armed Services Committee, The Future of Defense Task Force Report (Washington, DC, House Armed Services Committee, 2020).