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Author: Michael Akoto

  • Understanding AI in Intelligence Gathering and Analysis

    Understanding AI in Intelligence Gathering and Analysis

    What is AI?

    Artificial Intelligence (AI) involves the use of machine learning algorithms and predictive models to process and analyze large datasets. Intelligence gathering, a critical component of national security and defense systems, refers to the collection and analysis of information to identify potential threats. Recently, AI has taken on a bigger role in U.S. intelligence systems, automating data collection and analysis processes that used to be completed manually. While some see this as a step towards more efficient and effective defense systems, others point to the potential for human rights violations and security breaches. 

    The Rise of AI in National Defense

    The strategic competition between the United States and China has elevated AI as a critical factor in national security and defense intelligence capabilities. Both nations are heavily investing in AI, aiming to outpace each other in developing technologies that could offer a decisive advantage in intelligence gathering. At the same time, AI’s role in geospatial intelligence is particularly evident in the ongoing Russo-Ukrainian conflict. AI has become an essential asset for analyzing data from various sensors, systems, and personnel in the field. It gathers data from combat scenarios in real-time and provides actionable intelligence to military operators. 

    The Case for AI in Intelligence Operations 

    Proponents of AI’s increasing role in intelligence and defense cite two key reasons for their support:

    • Enhanced Data Analysis: Proponents of AI in intelligence gathering highlight AI’s ability to process vast amounts of data quickly and with precision. For example, Scylla AI software, designed for security and defense applications, demonstrated threat detection accuracy exceeding 96% and significantly reduced false alarm rates when tested by the U.S Department of Defense. By integrating computer vision and machine learning algorithms, Scylla improved response times in critical defense and security environments. Additionally, supporters cite projects like the U.S. Defense Intelligence Agency’s SABLE SPEAR which successfully employed AI to identify illicit activities that traditional methods overlooked.
    • Complementary Capabilities: Supporters contend that by automating repetitive tasks and providing recommendations based on historical data, AI not only reduces human error, but can serve to complement human decision-making. They argue that AI systems are not completely replacing military operators’ autonomy to make decisions, but rather serving as a partner in decision making that can increase accuracy and decrease collateral damage in military operations.

    Criticisms of AI in Intelligence Operations

    AI’s growing role in intelligence and defense has drawn criticism for three main reasons:

    • The “Black Box” Problem: Critics argue that AI’s lack of transparency in decision-making, often referred to as the “black box effect,” presents a significant challenge. AI systems may behave unpredictably, especially when trained on biased data. Since it is very difficult for operators to discern how and why AI systems reach certain decisions, it is difficult to redirect AI systems after making decisions that are harmful.
    • Human Rights Consequences: Opponents say the “Black Box” problem can lead to errors in critical applications, rendering AI-driven decisions dangerous in combat. A notable example is the U.S. military’s AI-driven drone strike in Kabul on August 29, 2021. The AI system incorrectly identified a civilian vehicle as a threat, resulting in a tragic strike that killed 10 civilians, including 7 children. AI’s reliance on input data quality means that biased or flawed data can produce inaccurate and potentially deadly conclusions. In the context of military operations, these flaws could lead to increased civilian casualties or misidentification of combatants, violating international norms like the Geneva Convention.
    • Security Vulnerabilities: Centralized data analysis systems also increase the risk of cyberattacks. Critics point out that advanced AI systems could be exploited by malicious actors, raising concerns over the security of sensitive information used in intelligence operations.
    • Infrastructure and Cultural Resistance: Finally, critics hold that integrating AI into government systems requires significant resources and organizational overhaul. Transitioning to AI-based intelligence systems requires updates to infrastructure and organizational culture, and may result in layoffs of employees. The U.S. Department of Defense has faced difficulties in standardizing and integrating its vast array of data sources, hindering AI deployment across military branches. Additionally, resistance from personnel concerned about job displacement and AI’s role in decision-making has slowed the integration process.

    Weighing the Benefits and Risks

    The integration of AI into intelligence operations offers the potential for increased efficiency, enhanced data analysis, and improved threat detection. However, it also introduces serious concerns about data security, job security, and the human rights risks of unpredictable combat outcomes. Moving forward, the intelligence community will have to weigh these perks and drawbacks as it continues its push towards AI integration. 

  • Understanding the Investigatory Encryption Backdoors Debate

    Understanding the Investigatory Encryption Backdoors Debate

    Background: Encryption Backdoors in Law Enforcement Investigations

    Encryption is the process of encoding messages so that only authorized individuals can decode and access the content. Organizations rely on encryption to protect sensitive data from unauthorized access. However, an encryption backdoor is any method that allows someone, regardless of authorization, to bypass encryption and access data. Encryption is like a lock that secures messaging data, while encryption backdoors function like master keys, providing access to that data. 

    The debate around encryption backdoors gained popularity after the 2015 San Bernardino terrorist attack in which individuals who had previously pledged loyalty to a leader of ISIS on social media carried out a mass shooting. As part of their investigation, the FBI tried to access data from one perpetrator’s iPhone 5C, believing it could provide critical information. The phone was locked with Apple’s iOS 9, which included a security feature that erases data after several incorrect password attempts. The FBI pressured Apple to create an encryption backdoor to bypass their security features. Apple declined, leading to a court case that was eventually dropped when the FBI accessed the data via a third party. An encryption backdoor, if provided to the U.S. government, would have allowed law enforcement to bypass security barriers and access data on numerous devices. 

    Encryption backdoors are controversial because they are both useful to investigations and vulnerable to exploitation. Efforts have been made to propose safe implementations of encryption backdoors. Some suggest that law enforcement could access encrypted data only with a court-ordered warrant. Under these conditions, encrypted content would remain secure by default, but law enforcement would gain access when a valid warrant is issued.

    Other proposed safeguards include:

    • Abuse Detectability: Systems would create a public audit trail whenever a backdoor is used, allowing independent auditors to monitor and report misuse of backdoors.
    • Global Warrant Standards: Establishing global warrant policies to ensure consistency across legal systems and prevent misuse by courts or law enforcement agencies.
    • Cryptographic Enforcement: Technical solutions could ensure that the master key is unusable if certain conditions—such as an invalid warrant or missing audit trail—are not met.

    Arguments for Encryption Backdoors in Law Enforcement

    Advocates of encryption backdoors for law enforcement support this measure for two main reasons:

    • Law Enforcement Necessity: Proponents argue that encryption backdoors are essential for law enforcement to access digital evidence related to severe crimes, such as terrorism, child abuse, and drug trafficking. Backdoors prevent encrypted messaging spaces from becoming “lawless zones” where criminals can operate without fear of surveillance or investigation.
    • Costly Alternatives: Without backdoors, law enforcement must rely on more expensive and less efficient methods to gather intelligence. Proponents of encryption backdoors argue that these alternatives are not always scalable, and can place a financial burden on taxpayers.

    Concerns Over Encryption Backdoors in Law Enforcement

    The use of encryption backdoors by law enforcement agencies draws criticism for three key reasons:

    • Security Risks to Users: Critics argue that creating a handful of access points to encrypted data through backdoors makes encryption less secure for regular users. If an access point is compromised, it could be exploited by malicious actors, leading to extensive breaches of sensitive information. Additionally, criminals could still use other encryption tools that do not have backdoors, leaving lawful users more vulnerable while criminals remain protected.
    • Law Enforcement Effectiveness: Opponents point out that encryption backdoors might not significantly improve law enforcement’s effectiveness. Federal authorities make arrests in less than one percent of the approximately 350,000 cybercrime incidents reported to the FBI each year. With 1 in 4 American households affected by cybercrime, only a small fraction of victims report these incidents, leading to concerns that backdoors would not meaningfully enhance law enforcement’s ability to combat such crimes.
    • Constitutionality: Foreign Intelligence Surveillance (FISA) Court rulings have found that the FBI’s practices violated the Fourth Amendment due to repeated unauthorized searches and improper queries of Americans’ communications without a warrant, including violations of privacy laws under FISA Section 702. These findings have raised concerns about whether law enforcement agencies’ use of encryption backdoors are constitutional.

    Legislative Responses to the Debate

    The ongoing debate on encryption backdoors has led to legislative proposals such as the Lawful Access to Encrypted Data Act, which seeks to create a balanced approach between law enforcement access and privacy rights. Key provisions of this act include:

    • Promoting Secure Innovation: The bill encourages the development of encryption technologies that support lawful access while safeguarding user privacy and security.
    • Strengthening Public-Private Collaboration: The bill incentivizes cooperation between the government and technology companies to establish frameworks for lawful access to encrypted data during criminal investigations.
    • Maintaining Privacy and Security Balance: The bill proposes policies to address warrant-proof encryption, ensuring a balance between individual privacy rights and law enforcement capabilities in serious crime investigations.

    Conclusion

    The debate around law enforcement agencies’ use of encryption backdoors is ongoing, and revolves around the competing ideals of user privacy and investigatory efficacy. While encryption backdoors might assist ongoing criminal investigations, they can also pose significant risks to user privacy and data security. As legislators continue to address the issue through national policy, the question remains: should law enforcement have the ability to access encrypted data, or should individual privacy come first?

  • Michael Akoto, Rutgers University

    Michael Akoto, Rutgers University

    Michael is currently pursuing a Bachelor’s degree in Computer Science at Rutgers University, with an expected graduation in May 2026. His academic background includes a comprehensive range of courses, such as data structures, algorithms, discrete structures, statistics for research, and linear algebra. This has fostered a strong foundation in computational thinking and problem-solving, which aligns with his growing interest in cybersecurity and data analysis. Professionally, Michael has gained valuable experience as a Customer Service Representative at Burlington, where he honed his communication and organizational skills. He also volunteered at the University of Allied Health Sciences in his home country Ghana, contributing to meaningful research in the student community. Michaels research interests lie at the intersection of cybersecurity, data protection, and information technology. He is particularly drawn to the evolving landscape of cybersecurity threats and how they intersect with national security. This focus drives his desire to develop robust systems that safeguard sensitive information and critical infrastructures. He is also interested by the ethical and societal implications of data security, particularly in governmental roles where the protection of public and national data is important. Beyond academics, Michael enjoys making beats and exploring creative avenues in music production. 

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