The Intelligence Edge: Opportunities and Challenges from Emerging Technologies for U.S. Intelligence

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  • Emerging technologies such as artificial intelligence have the potential to transform and empower the U.S. Intelligence Community (IC) while simultaneously presenting unprecedented challenges from technologically capable adversaries.

  • These technologies can help expand, automate, and sharpen the collection and processing of intelligence, augment analysts’ ability to craft strategic and value-added analysis and insights, and enable the IC to better time, tailor, and target intelligence products for key decisionmakers.

  • U.S. rivals and adversaries are also moving swiftly to develop, field, and integrate these technologies into intelligence operations against the United States. In addition to competing with state rivals, the U.S. IC also must overcome its own bureaucratic, technical, and organizational hurdles to adopting and assimilating new technologies.

  • The CSIS Technology and Intelligence Task Force will work to identify near-term opportunities to integrate advanced technologies into the production of strategic intelligence and craft an action plan to overcome obstacles and implement change.


Maintaining a competitive advantage in strategic intelligence over increasingly sophisticated rivals and adversaries will be a critical component of ensuring and advancing U.S. national security interests in the coming decades. Central to success in the intelligence realm will be the adoption and assimilation of emerging technologies into the way intelligence is collected, analyzed, and delivered to decisionmakers. If intelligence is about providing timely, relevant, and accurate insight into foreign actors to provide U.S. leaders an advantage in formulating policy, then many new technologies hold the potential to unlock deeper and wider data-driven insights and deliver them at greater speed, scale, and specificity for consumers. These same technologies, however, will also transform the intelligence capabilities of rivals such as China and Russia and could disrupt the very fundamentals of U.S. intelligence.1 In competition with such rivals, emerging technologies and their application to intelligence missions will be a primary and critical battlefield.

The CSIS Technology and Intelligence Task Force has embarked on a year-long study to understand how technologies such as such as artificial intelligence(AI)I and its subset, machine learningII (ML), cloud computing, and advanced sensors, among others, can empower intelligence and the performance of the intelligence community (IC). The task force will explore how emerging technologies can be applied and integrated into the IC’s day-to-day operations and how the IC must adapt to maintain their intelligence edge.

This CSIS Brief provides a strategic framework for the task force’s efforts. It begins with a snapshot of the potential opportunities emerging technologies present across the intelligence cycle, to be explored in greater depth during the project year. It then outlines the risks and challenges such technologies will pose to the IC. The brief concludes by presenting the core intelligence questions that will drive the task force’s inquiry. The focus of this framing brief and the CSIS task force is strategic intelligence, that is, nation- al-level intelligence intended for senior-level policymakers and national security officials.


Emerging technologies are already reshaping how the IC gathers, stores, and processes information but will likely transform all core aspects of the intelligence cycle in the coming decades—from collection to analysis to dissemination. Driving this change is the convergence of four technological trends: proliferation of networked, multimodal sensors; massive growth in “big data,” both classified and unclassified; improvements in AI algorithms and applications particularly suited to intelligence, such as computer vision and natural language processing; and exponential growth in computing power to process data and power AI systems.III,2

As the U.S. private sector drives these advances, the IC’s ability to combine commercial AI applications with IC-unique data and systems creates unprecedented opportunities for improving how the IC collects, processes, and derives meaning from data and delivers actionable insights to policymakers.3 However, as we consider the opportunities presented by emerging technologies like AI, it is also important to understand that these technologies are neither silver bullets to intelligence tasks and problems, nor independent from a much broader technology and human capital ecosystem.

Emerging technologies are already reshaping how the IC gathers, stores, and processes information but will likely transform all core aspects of the intelligence cycle in the coming decades—from collection to analysis to dissemination.


In a world of proliferating sensors and exponential growth in data and computing, AI can help enable intelligence collection organizations in automating and simplifying the processing of collected data and in identifying and prioritizing collection targets across the various “-INTs”—geospatial (GEOINT), signals (SIGINT), human (HUMINT), and open-source (OSINT). AI applications can then assist analysts in how they receive, visualize, and exploit that data to discern insights for policymakers.

  • Technical Collectors: AI is particularly well-suited for more technical means of collection such as SIGINT and GEOINT, helping process and analyze their massive pools of sensor-derived data.4 For GEOINT, AI capabilities such as computer visionIV can help automate the processing of reams of imagery data and perform critical, time-intensive tasks, such as image recognition and categorization at speed and scale.5 For SIGINT, AI can be similarly useful in automating the processingV of electronic signals data (ELINT), while speech-to-text translation/transcription and other natural language processing capabilities help decipher intercepted communications (COMINT).6

  • Human Operators: In addition to the technical “-INTs,” AI tools can also enable the on-the-ground human operator in the most core HUMINT mission: recruiting and deriving intelligence from foreign agents.7 AI algorithms could be trained to help “spot and assess” potential sources by combing open-source data. Advanced analytics could then help construct “digital patterns-of-life” of these recruitment targets, assisting in predicting their activities and verifying their access to desired information.8 These tools could then be used to monitor for security and counterintelligence risks before and after recruitment.9

  • Commercial Partners: While enabling aspects of classified intelligence collection, emerging technologies will also transform open-source intelligence (OSINT), providing the IC high-quality data streams and freeing up “exquisite” collection platforms for harder intelligence targets. The commercialization of space and proliferation of satellite-based sensors will dramatically improve the coverage and quality of commercial imagery and some signals collection.10 The availability of big data and OSINT-derived analytics on global security, political, and economic trends can also help alleviate the collection burden on the IC’s small HUMINT cadre and allow them to focus on collecting truly secret information.

While enabling aspects of classified intelligence collection, emerging technologies will also transform open- source intelligence (OSINT), providing the IC high-quality data streams and freeing up “exquisite” collection platforms for harder intelligence targets.


Emerging technologies can also transform and augment how analysts make sense of ever-growing data and team with machines to deliver timely insights to decisionmakers. “The future of analysis,” CIA’s former Chief Learning Officer Joseph Gartin writes, “will be shaped by the powerful and potentially disruptive effects of AI, big data, and machine learning on what has long been an intimately scaled human endeavor.”11 Disruption can be a positive for analysts and the way analysis is generated. Analysts could harness AI to more efficiently find and filter evidence, sharpen and test their judgements with machine-derived ones, and automate simple and necessary but time-absorbing tasks. The result could be an analytic cadre with more strategic bandwidth and better able to exploit what will remain their “intimately human” advantages in applying context, historic knowledge, and subject matter expertise to identifying implications and opportunities for policymakers.12

  • Smarter Search, Fusion, and Data Visualization: Analysis starts with the search for relevant reporting and data across the “INTs.” Analysts should be able to leverage AI, including deep learning,VI to help sift through reporting streams to identify and visualize patterns, trends, and threats and integrate them into their analysis.13 With AI, strategic analysts and data scientists could partner to hone smarter queries and search algorithms for a given intelligence question, casting wider, more creative, and more efficient nets across datasets to piece together critical but often non-explicit information (e.g., “what is adversary X’s strategy for Y?”)14.

  • Testing Analytic Lines: As intelligence professionals build their analytic lines, assembling key evidence derived from the “INTs” and forming initial judgments, data analytics can be leveraged to test those initial findings against big data and machine-derived results.15 Corroboration can strengthen analytic lines, while conflicting findings can push analysts to revisit their evidence and assumptions. Machine knowledge and judgment of past analytic lines, source quality, and competing hypotheses can add rigor to the process, helping analysts confront bias, avoid groupthink, think critically, and be transparent about their levels of confidence.16

  • Offloading Analytic Tasks: In addition to providing inputs for analysis, AI tools can also perform certain types of analysis, enabling analysts to offload more tactical or time-intensive tasks onto machines. Even today, all-source analysts are still called upon to craft daily intelligence products monitoring crises and summarizing geopolitical events when AI can cull the same data—often primarily open-source—and generate written summaries.17 Machines could also supplement, aggregate, or substitute for analysts in areas where the IC has a mixed tracked record and unclear comparative advantage, such as predictive analysis and long-range forecasting.18

Analysts should be able to leverage AI, including deep learning, to help sift through reporting streams to identify and visualize patterns, trends, and threats and integrate them into their analysis.


Emerging technologies can help transform not only the crafting of intelligence but also how it is delivered to decisionmakers—at the time, place, and level needed to have impact and stay ahead of the decision curve.19 As the AIM Initiative notes, cloud computingV11 and IC digital infrastructure have “paved the road to harness the power of unique data collections and insights to provide decision advantage at machine speed.”20 Beyond product dissemination, cloud and AI tools can help transform how intelligence is shared and delivered more broadly—between analysts, organizations, and allies—to distribute vital knowledge and inform decisionmaking.21

  • Customization: As cloud and AI are distributed and used across IC and policymaking organizations, analysts should be able to better time, tailor, and target products to diverse sets of consumers according to their unique intelligence needs.22 Much like AI can help analysts process and prioritize relevant data, these tools could help consumers prioritize which intelligence products they receive, customizing their daily “readbooks” to serve their current policy and operational needs. Global cloud capabilities could also help analysts deliver customized intelligence to more decisionmakers—military, diplomatic, and intelligence operators—in more places around the world, unlocking new customers for their products.23

  • Collaboration: In addition to delivering finished intelligence products, cloud and AI can enable analysts to collaborate more efficiently and effectively across geographic locations in generating those products.24 Analysts could leverage common or accessible

data architectures to share data sets, train and test algorithms, and jointly employ AI tools to generate insights, convey knowledge, and coordinate analytic lines across more diverse sets of analysts.25 Cloud- enabled collaboration could strengthen analytic findings, build shared missions, and provide consistent feedback to collectors, even those operating on the edge.26

  • Sharing: Cloud and AI could also be leveraged to improve intelligence and information sharing with customers, consumers, and constituents outside the IC. Within the U.S. government, multi-layer fabrics and cloud architectures could enable the IC to more easily and securely share information with policy, military, and law enforcement organizations at differing classification levels.27 Outside government, cloud and data sanitization tools could assist the IC in sharing sensitive but unclassified information with the private sector on matters of vital importance, such as cyber threats to critical infrastructure and disinformation campaigns on social media platforms.28 Outside the United States, cloud and AI can also improve how intelligence is shared and jointly developed over time with U.S. allies and partners.29

Beyond product dissemination, cloud and AI tools can help transform how intelligence is shared and delivered more broadly—between analysts, organizations, and allies—to distribute vital knowledge and inform decisionmaking.


While the benefits of emerging technologies could be immense for American intelligence, their development, of course, will not occur in a geopolitical vacuum. U.S. rivals, namely China, but also Russia, are moving swiftly to develop, field, and integrate similar AI and associated technologies into intelligence operations. The challenge to U.S. intelligence, however, will come not only from U.S. adversaries but from the IC itself, as organizational, bureaucratic, and technical hurdles slow technological adoption. Further challenges will come from the competition of the private sector and the increasing quality of open-source intelligence, which may be just as—or more—timely, relevant, and accurate than what the classified intelligence world generates.


As the international race for dominance in AI accelerates, battlefield advantage, the AI National Security Commission notes, “will shift to those with superior data, connectivity, compute power, algorithms, and overall system security.”30 That battlefield will extend beyond the military realm and into to the intelligence one as AI and associated technologies permeate intelligence operations. In the evolution to “intelligentized” warfare, as Chinese military strategists describe it, China, Russia, and other rivals will enjoy a structural advantage: unity of civilian-military effort in developing and employing AI technologies.31 This resource advantage will be exploited to strengthen their defenses against U.S. intelligence operations and enable more targeted and aggressive offensive operations.

  • Faster to the Fight: China is betting that its whole-of- nation strategy for AI development, fusion of military and civilian spheres, and “techno-utilitarian political culture,” as Kai-Fu Lee writes, “will pave the way for faster deployment of game-changing technologies,” providing a distinct advantage in fielding these technologies for intelligence missions at speed and scale.32 China, Russia, and other authoritarian states’ ability to synthesize civilian and military AI R&D and steer commercial sector innovation to military and intelligence applications enable them to pool national resources and know-how and potentially adapt technology more quickly to changing operational environments.33 China’s continuing advances in 5G and internet-of-things will enable even faster distribution and use of AI-enabled intelligence tools, for both defense and offense.34

  • Stronger Defense: AI-enabled intelligence tools will assist China, Russia, and other U.S. rivals seeking to disrupt, deny, and deceive U.S. intelligence collection. A world of “ubiquitous surveillance” due to advances in AI surveillance and biometric tools will create more denied areas for HUMINT operations, persistent risk of exposure, and the need to change or discard decades of well-honed tradecraft.35 AI- enabled advances in cybersecurity and cryptography and, in the future, quantum computing, could enable adversaries to harden and encrypt their systems to deny penetration of and collection on their networks.36 Deception techniques to fool algorithms into misclassifying data and use of generative adversarial networksVIII to create “deepfakes” of imagery, communications, and intelligence reports could sow confusion among U.S. analysts, leading to poor analysis and misinformed policy and operational decisions.37

  • Aggressive Offense: AI tools will likely also be exploited to penetrate, manipulate, and weaken U.S. collection and analytic capabilities. AI-accelerated cyberattacks could target collection and communication platforms and employ intelligent malware to access, exploit, or destroy critical data and intelligence.38 Once inside, foreign intelligence could exploit “counter- AI” techniques to insert “poisoned” or false data into training sets to fool U.S. IC algorithms and cause AI systems to misperform, such as a deep neural network image classifier falsely recognizing friend as foe.39 In addition, AI-enabled disinformation campaigns will enable adversaries to propagate false information at unprecedented scale and seeming authenticity, sowing confusion for analysts and policymakers attempting to make sense of and take action on information.40

AI-enabled intelligence tools will assist China, Russia, and other U.S. rivals seeking to disrupt, deny, and deceive U.S. intelligence collection. . . . AI tools will likely also be exploited to penetrate, manipulate, and weaken U.S. collection and analytic capabilities.


The coming decade will provide no shortage of tech-enabled opportunities to advance U.S. intelligence, but organizational and bureaucratic barriers and the security and technical realities of intelligence and data architecture will likely hinder the IC’s ability to exploit them. Cutting-edge technology might exist for a given intelligence mission, but it could be outdated and surpassed by U.S. rivals by the time it is actually acquired and integrated. Quality data might exist but cannot be turned into insights and action if it cannot be shared or accessed by analysts. And even if data can be shared, analysts might not trust it or related findings derived by machines.

  • Procurement and Adaptation: The IC’s technology procurement timelines tend to be in years, while the cycle of innovation in the commercial sector renders those technologies outdated in months. The IC’s lengthy research, development, testing, and evaluation timeline reflects its unique needs, risks, and security requirements but will hinder its ability to acquire, integrate, and assimilate AI technologies at speed—let alone at scale and cross enterprises.41 Moreover, procurement and contracting practices will also make it difficult to adapt acquired AI technologies and restructure key tasks, such as retraining ML algorithms, to shifting intelligence needs and operational environments.42

  • Stovepipes and Silos : AI tools need access to training and validation data sets across all INTs to be useful for all-source analysts, but vital data often remains hidden in silos buried across IC organizations or on inaccessible data architecture that prevents sharing.43 Even if data can be accessed and shared, most useful AI methods for intelligence applications require large, quality, and consistently tagged data sets, but differing labeling standards and practices across and even within agencies means analysts still have to do much of the time-intensive processing and collating work.44 The challenges of data access, architectures, access, and formatting are only further exacerbated when working with foreign partners.45

  • Trust, Authentication, and Explainability: Despite the scale of classified collection, the IC will likely still need access to commercially derived data to have sufficient volume to train and power AI applications. But unlike the commercial sector, IC analysts and data scientists cannot easily turn to open or crowdsourcing platforms for labeled data nor will they necessarily trust the accuracy and authenticity of either, particularly as China and Russia launch more aggressive adversarial AI efforts.46 Analysis depends on clear explanations and reasoning for the logic, evidence, assumptions, and inferences used to reach conclusions. Machine- generated analyses derived from blackbox algorithms will be unusable if analysts are unable to understand the logic and processes behind the conclusions and the conditions under which they are valid.47


Along with external threats and internal obstacles, the IC will face a more fundamental, even existential, challenge from rapid technological advances: if commercialization of the intelligence playing field means the information and tools once exclusively the domain of government are made widely available, what will be the IC’s purpose and missions? Technological transformation will also force the IC to more clearly and demonstrably justify its cost and value-added—to policymakers, to Congress, and to the American people—in an environment of growing skepticism, misinformation, and public assaults on the IC’s integrity. Challenges to core missions will be felt by individual professionals, within specific IC organizations, and across the intelligence community writ large.

  • Organizations: U.S. intelligence collection and analysis organizations have been designed around specific “INT” and analytic missions, building unique expertise and cultures over the decades. The blending of intelligence missions through the nature of AI and technological advances (e.g., HUMINT operators using their own SIGINT and GEOINT tools, or AI SIGINT processing tools also generating analysis) could render such task organization irrelevant or ill-suited to future missions. Moreover, competition for intelligence missions with equal or superior commercial products could render entire IC organizations irrelevant, redundant, or even obsolete.

  • Personnel: Within intelligence organizations are intelligence professionals; in an AI-augmented workplace, who will be recruited and attracted to join the IC? Alternatively, how will non-tech savvy career officers be retrained and retooled to succeed?48 Will case officers and political analysts who spent a decade studying Arabic, the Middle East, and intelligence tradecraft also need to learn how to code? The fundamentals of what an intelligence professional is and does is likely to change dramatically. Current officers will be required to prepare for a tech-driven future while still mastering present day missions and tasks.

  • The IC Itself: The proliferation and increasing quality of AI-enabled open-source collection and data analytics tools means that quality analysis of global events and specific threats can be generated for U.S. policymakers at a fraction of the IC’s cost. And while “exquisite” intelligence platforms will still be needed to collect on hard targets and true secrets, the persistent risk of hacks, cyberattacks, and leaks means these expensive tools can be more easily stolen, denied, and rendered inoperable by U.S. adversaries, negating their value.49

Along with external threats and internal obstacles, the IC will face a more fundamental, even existential, challenge from rapid technological advances.


While the risks and challenges of emerging technologies to U.S. intelligence are formidable, the opportunities to harness them will likely be even greater. In the months ahead, the CSIS Technology and Intelligence Task Force will be focused on identifying those opportunities and the policy, legislative, organizational, and technical changes that must occur to effectively seize them. Our core objective is to generate actionable recommendations to help the U.S. IC remain the global gold standard in crafting and delivering strategic intelligence that provides policymakers advantages over U.S. adversaries. The central “intelligence question” driving the task force’s research will be:

What are the near-term opportunities to integrate advanced technologies into the production of strategic intelligence, and how can the obstacles to doing so be overcome?

The key sub-questions the task force will explore include:

  • Which emerging technologies could be most relevant and impactful across and within each means of collection (e.g., SIGINT, GEOINT, and HUMINT)?

  • How can “analyst-machine” performance be optimized to maximize data intake, streamline processing, prioritize relevant information, and create more bandwidth for analysts to think and write strategically?

  • How can emerging tech such as AI and cloud computing be used to improve collaboration, coordination, and delivery of intelligence products to policy, intel, military, and allied customers?

  • What is the right model for deploying data scientists and technologists into all-source analysis environments, and what skills should or must strategic analysts develop?

  • Where can the IC smartly focus its technology investments, and what is best to leave to the commercial sector? How can the IC be more agile in acquiring and assimilating them?

  • What are the implications of success or failure in incorporating emerging technologies into the U.S. intelligence enterprise for U.S. national security vis- à-vis global competitors?

Our working hypothesis is twofold. First, emerging technologies hold incredible potential to augment, improve, and transform the collection, analysis, and delivery of intelligence but could require fundamental changes to the types of people, processes, and organizations conducting the work. Calls for entire new entities or other “org chart” solutions, however, will not solve the problem, nor ensure technological advances are actually being integrated at the working-level to truly augment performance. Second, while ML and commercial applications will make some IC tasks and personnel unnecessary or obsolete, the unique skills and expertise of IC professionals remain a distinct U.S. advantage, able to generate intelligence unrivalled in insight, context, and foresight.

Thus, the IC and its critical supporting elements— policymakers, Congress, the technology and industrial sectors, and the research community—should focus on developing and integrating technologies that best enable and augment the IC’s value-added: collecting vital and truly secret intelligence and crafting datadriven, context-rich, and forward-looking analysis that is consistently higher on the policymaker value chain than that of its rivals.50 Failing to do so risks a reactive U.S. national security policy apparatus that is consistently unable to advance the nation’s strategic interests in the face of determined adversaries.

Brian Katz is a fellow in the International Security Program at the Center for Strategic and International Studies (CSIS) and research director of the CSIS Technology and Intelligence Task Force.

This report is made possible by support to the CSIS Technology and Intelligence Task Force from Booz Allen Hamilton, Rebellion Defense, Redhorse, and TRSS.

CSIS Briefs are produced by the Center for Strategic and International Studies (CSIS), a private, tax-exempt institution focusing on international public policy issues. Its research is nonpartisan and nonproprietary. CSIS does not take specific policy positions. Accordingly, all views, positions, and conclusions expressed in this publication should be understood to be solely those of the author(s).

© 2020 by the Center for Strategic and International Studies. All rights reserved.

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Brian Katz