AI and National Security: Promise and Peril

Artificial Intelligence isn’t just transforming how people work, communicate and seek out information. It’s rapidly revolutionizing the way nations protect themselves. For national security and intelligence agencies, AI offers incredible potential: the ability to analyze information on a scale human analysts simply can’t match and ultimately reach faster insights and make smarter decisions. 

But there’s another side to the story. The same technology that can help keep us safe is also being used by bad actors to spread disinformation, launch cyberattacks and disrupt economies. As a result, countries are challenged to keep one step ahead by leveraging the vast power of AI to detect and neutralize these threats.

Read on as we examine how AI for national security is a story of both promise and peril.

The Value of AI for National Security Agencies

AI holds enormous potential for strengthening national security. The challenges of modern intelligence analysis, including vast and diverse volumes of data, complex threat environments and the need for rapid responses, all correspond closely with AI’s strengths.  Examples of how AI can enhance national security include:

Improved Intelligence Analysis

Intelligence analysts, like all humans, are prone to cognitive biases. These biases are systematic errors in thinking that occur when individuals process and interpret information. In the context of intelligence analysis, they can undermine objective analytical judgement and lead to flawed conclusions. In a recent academic conference titled “Intelligence in the Modern Era”, Prof. Frederic Lemieux from Georgetown University presented how AI-powered tools can assist intelligence analysts in identifying and mitigating their own cognitive biases, which will result in more accurate and higher quality intelligence analysis.

At the same conference, Dr. Monica Robbins, also from Georgetown University, presented analysis on how Large Language Models (LLMs) can serve as “virtual brainstorming partners” for intelligence analysts enabling them to think outside of the box. According to Dr. Robbins, generating hypotheses is one of the most critical but overlooked parts of intelligence analysis. LLMs can help analysts counter siloed thinking to construct hypotheses they would not come up with on their own.

Processing the Information Overload

National security agencies face a torrent of incoming data: communications, satellite imagery, financial records, sensor outputs, open-source content and much more. Human analysts cannot possibly sift through it all. AI can prioritize what really matters, surfacing insights from the noise.

Predictive Threat Analysis

AI doesn’t just help react to threats; it can anticipate them. By analyzing historical patterns, geopolitical events and adversary behaviors, AI systems can predict potential risks before they materialize. For instance, AI can identify subtle signs of military buildups, coordinated disinformation campaigns or financial anomalies that precede cyberattacks.

How Bad Actors Are Using AI

For national security agencies, the challenge is keeping pace with adversaries who exploit AI’s low-cost, high-impact potential. AI makes attacks faster, cheaper and harder to defend against. Unfortunately, bad actors don’t need large budgets or sophisticated skills to exploit the power of AI. Technology democratization has lowered the barriers to entry. With cutting edge AI models available to the public freely or for a small fee, advanced AI capabilities are within reach of anyone with a broadband connection and a credit card.

Some of the most concerning uses of AI by bad actors include:

• Hybrid warfare: Disinformation has become one of the most potent non-kinetic tactics used in hybrid warfare, enabling adversaries to manipulate public perception and disrupt societies. Fabricated video or audio clips generated by bad actors can spread rapidly, sowing confusion or conflicts. For example, a deepfake video circulated in 2022 showed Ukrainian President Volodymyr Zelensky urging his troops to surrender. Though quickly debunked, the clip highlighted how rogue states can use AI-generated disinformation to destabilize public trust in moments of crisis, thus furthering their nefarious agenda. In addition, GenAI tools powered by large language models (LLMs) can rapidly churn out persuasive fake content in multiple languages, amplifying influence campaigns through bot networks across social media. 
• Terror recruitment and incitement: AI enables extremist groups to easily mass- produce tailored propaganda, using realistic synthetic images and deepfakes together with convincingly written texts, social media posts and private messages. They then disseminate these using fake bots to amplify the messages to vulnerable audiences, accelerating both recruitment and incitement of violence.
• Cyberattacks: Attackers are now using LLM-powered chatbots to scale their cyberattacks by automating the creation of phishing emails, allowing bad actors to generate convincing messages in any language without fluency. Hackers also deploy AI-powered tools to discover vulnerabilities faster and design malware that learns how to evade detection.
• Attack planning and targeting: By analyzing vast datasets, adversaries can identify weaknesses in critical infrastructure, financial systems and supply chains.

AI-Powered Solutions That Can Tip the Balance

The AI domain is broad and encompasses a variety of technologies, including natural language processing (NLP), computer vision, Generative AI (GenAI), machine learning and neural networks. But it’s not enough to have AI technology: the trick is implementing it within investigative solutions which are tailored both to intelligence methodologies and to the data sources which intelligence agencies leverage in their investigations and research. Solutions which are particularly relevant for national security include:

Network Intelligence – SIGINT

Signals intelligence remains the backbone of intelligence operations. AI enhances SIGINT by accelerating the analysis of communications data, and generating vital intelligence from it using tools such as speech-to-text transcription, voice detection, translation and sentiment analysis. This enables agencies to identify suspicious patterns and behaviors in ways that would be virtually impossible to achieve manually.

Blockchain Intelligence

Terrorist groups and extremists increasingly use cryptocurrencies for terror funding, from soliciting donations to covertly moving funds across borders. AI-powered blockchain intelligence tools can enable authorities to map suspicious crypto transactions and directly link crypto wallets used for illicit activities to their owners.

Open-Source Intelligence (OSINT)

Publicly available data, from social media posts to news media and commercial databases, has become a critical part of the intelligence picture. AI can accelerate the gathering, translating and analyzing of OSINT content to identify trends and anomalies that might otherwise go unnoticed. For example, in the war in Ukraine, analysts have often used AI to process TikTok videos, satellite photos and local news posts and were able to build a near real-time picture of troop movements.

Decision Intelligence

Decision intelligence platforms provide analysts with a holistic intelligence view by fusing, correlating and analyzing data from diverse sources and formats, including text, audio, images and videos. By leveraging AI technologies, these platforms go beyond traditional analytics to reveal hidden relations and patterns, as well as uncover anomalies and suspicious indicators.

Generative AI Co-Pilots

Generative AI (GenAI) is emerging as a powerful “co-pilot” for analysts and decision-makers. GenAI tools enable analysts to explore large datasets using natural language queries and can even suggest additional lines of inquiry and suggested next steps. This enables authorities to accelerate investigations and improve operational outcomes. Importantly, co-pilots are not autonomous decision-makers but aids that enhance human capabilities.

Cyber Intelligence

AI-driven cyber security solutions can spot unusual network traffic patterns in real time, giving defenders a head start before intrusions spread across networks. By detecting and mitigating hidden threats, security teams can contain threats more effectively before they escalate and ensure protection against advanced persistent threats (APTs), nation-state actors and other threat actors.

Use Cases for AI in National Security

AI applications for national security are diverse and reflect the multifaceted nature of today’s threats. Here are some of the most promising AI-driven use cases today:

• Counterterrorism: Through data fusion and predictive analytics, AI helps law enforcement and intelligence agencies identify extremist networks, track the flow of illicit funding and anticipate attacks before they are carried out.
• Intelligence operations: AI technologies are widely used today to help track down suspects and uncover critical evidence. AI delivers value across multiple domains, such as pattern recognition, real-time video analytics and enrichment engines that transform raw data into usable information.
• Border protection: AI-enhanced drones and unmanned aerial vehicles (UAVs) analyze vast areas, using machine vision, object detection and behavioral analytics to scan remote areas for illegal activity, helping authorities cover terrain that is otherwise impossible to observe in real time.
• Cyber defense: AI strengthens cybersecurity by identifying abnormal network traffic patterns, detecting zero-day exploits and automating responses. Adaptive AI systems can learn from evolving threats, reducing the reaction time between intrusion detection and containment.

Concerns We Must Not Ignore for AI and National Security

For all its promise, AI has the potential for misuse, and its power must be managed responsibly and ethically. National security organizations must consider several key aspects when putting AI into practice including:

• Bias and fairness: AI models are trained on volumes of historical data, which may contain unconscious biases. These models in turn can produce biased results.
• Transparency: Many AI systems are “black boxes,” producing results without revealing how those conclusions were reached. Intelligence agencies must use AI systems designed with model explainability, meaning they make their reasoning and decision pathways visible to human operators. In high-stakes national security contexts, leaders need to know why AI flagged a specific suspect or recommended a course of action.
• Sycophancy: One of the pitfalls of GenAI models is that they aim to ‘please’ the user, which may result in telling the user what they want to hear rather than the factual truth. When a system’s goal is geared towards appeasement rather than accuracy, this can lead to the system providing incomplete answers as well as inaccurate summaries and answers.

Finding the Balance

AI is reshaping the national security landscape with both promise and peril. Adversaries are exploiting the technology for disinformation, cyberattacks and operational advantage. Yet the same tools can empower intelligence agencies to process data at scale and make smarter decisions. AI-powered investigative solutions, such as SIGINT, blockchain analytics, OSINT and GenAI co-pilots, hold immense potential for national security.

At the same time, concerns around bias and responsible use are of paramount importance. The key lies in striking the right balance: harnessing AI’s strengths while mitigating its risks. As AI continues to evolve, national security agencies that adopt it thoughtfully and responsibly will not only protect their nations more effectively but also preserve the principles that define them.

Contact Cognyte to explore how AI-powered solutions give countries the upper hand in countering threats to national security

FAQs

How does AI help intelligence analysts overcome human cognitive biases?

AI-powered tools assist analysts by identifying and mitigating systematic errors in thinking that lead to flawed conclusions. Additionally, Large Language Models (LLMs) act as virtual brainstorming partners, helping analysts generate diverse hypotheses and “think outside the box” to counter siloed mentalities.

In what ways are bad actors currently exploiting AI for hybrid warfare?

Adversaries use Generative AI to spread high-impact disinformation through deepfake videos and audio, manipulating public perception to destabilize societies. Furthermore, LLMs allow bad actors to scale influence campaigns by rapidly churning out persuasive fake content and managing bot networks across social media.

How can AI-powered blockchain intelligence disrupt terror funding?

Because extremist groups increasingly use cryptocurrency to move funds covertly, AI-driven blockchain tools are used to map suspicious transactions. These tools can directly link illicit crypto wallets to their owners, enabling national security agencies to track and neutralize financial networks.

What is the role of a “GenAI Co-Pilot” in an investigative context?

A GenAI co-pilot serves as an analytical aid rather than an autonomous decision-maker. It allows investigators to explore massive datasets using natural language queries, suggests new lines of inquiry, and accelerates the time it takes to transform raw data into actionable intelligence.

What are the primary ethical risks of using AI in national security?

Key concerns include bias, where models inherit flaws from historical data; transparency, where “black box” systems produce results without explaining the reasoning; and sycophancy, a phenomenon where AI models prioritize “pleasing” the user over providing factual, accurate truth.