Artificial Intelligence (AI) in Cybersecurity

Introduction

AI (artificial intelligence) is the technology industry’s current buzzword. In today’s modern world, AI is becoming a part of all industries, including healthcare, self-driving cars, and cybersecurity. Due to the nature of modern businesses, they have significantly more technology in use, which makes them a bigger target for cyber criminals. Many organizations and governments are turning to AI within their cybersecurity posture to aid cybersecurity analysts and even the playing field. However, AI is not without its problems. There is a fine line between success and failure that will allow AI to grow with us, and not be a hindrance.

Current Uses of AI in the Federal Government

According to “AI.gov,” the government website is dedicated to showcasing how the government is using AI for the public’s benefit; the United States can greatly benefit from harnessing AI’s opportunities (n.d.). The federal government uses AI “to better serve the public” from healthcare, to transportation, to the environment, and improved delivery of parcels (White House, n.d.). Also, there are also very important cybersecurity use cases that the Cybersecurity Infrastructure and Security Agency (CISA) are using to protect the United States and its networks.

The “2023 Consolidated AI Use Case Inventory” describes how CISA, in partnership with the Department of Homeland Security (DHS), is given terabytes of data per day from the “National Cybersecurity Protection System’s (NCPS) Einstein sensors” (White House, 2023). Threat hunters and Security Operations Center (SOC) analysts then comb through this data and use AI-driven “automated tooling to further refine the alerts” looking for abnormalities (White House, 2023). AI allows for humans to be more efficient and handle larger amounts of data without additional personnel.

At a higher level, CISA’s Operations Center utilizes a dashboard that is “powered by artificial intelligence to enable sensemaking of ongoing operational activities” (White House, 2023). The dashboard ingests data from “open-source reporting, partner reporting, CISA regional staff, and cybersecurity sensors” to provide near-real-time event data (White House, 2023). This is then used to provide recommended “courses-of-action and engagement strategies with other government entities” to protect critical infrastructure and other National Critical Functions (NCFs) (White House, 2023). NCFs are functions considered so vital and important to the Federal Government that their disruption or interruption “would have a debilitating effect on security, national economic security, national public health or safety, or any combination thereof” (CISA, n.d.). Within the Federal Government, DHS is the leader in its use of AI in cybersecurity. This is how CISA helps to achieve defense-in-depth, the cybersecurity principle of multiple overlapping security practices to achieve a more secure network, for United States’ networks.

Current Uses of AI in the Private Sector

The private sector is simultaneously innovating the cybersecurity industry with AI. Everyday new uses are popping up that are improving an organization’s cybersecurity posture, by allowing them to analyze and respond faster, and giving it a better chance of catching attacks and preventing harm. In the realm of cybersecurity, AI is being used to prevent fraud, data loss protection (DLP), and cloud computing.

One of the newer use cases is using AI to prevent fraud. AI models can be trained on what is expected of users in terms of spending habits, the amount of time and distance covered between transactions, and what abnormal behavior looks like. Tyler Adams, from CertifID, in his work “How AI is Revolutionizing Fraud Detection,” argues that for large institutions that are “processing large transactions” and do not take the proper precautions, the consequences can be disastrous (2024). Therefore, fraud detection tools like this are critical for financial institutions seeking to achieve defense-in-depth.

Data Loss Prevention (DLP) is part of a company or organization’s cybersecurity strategy to enforce its data handling policy. It can be configured to recognize sensitive company information or personally identifiable information (PII) preventing unauthorized disclosure. Machine learning (ML), a branch of AI, is being implemented into DLP tools to make them more accurate and respond faster.

With the growing adoption of cloud computing, many organizations are uploading vast amounts of data to the cloud. Sometimes, sensitive data meant to remain on premises is accidentally uploaded to the cloud. Unless proper precautions are implemented, this information is now vulnerable from transport to shared storage. Storing such information on a cloud platform is a poor security practice. ML DLP tools can quickly parse through such information and ensure that it is properly classified or prevented from uploading to the cloud (Glynn, 2024). Similarly to fraud detection tools, ML DLP tools can be trained to “discern behavior patterns to detect attempted unauthorized access[es]” (Glynn, 2024). AI can improve preexisting cybersecurity tools, help organizations and companies achieve defense-in-depth, and provide a greater cybersecurity posture.

Strengths of AI in Cybersecurity

When AI and cybersecurity cross paths, organizations benefit from many strengths. Through proper training, AI can automate simple repetitive human tasks and procedures like information sorting and report generation (Marr, 2024). These tasks may be simple, but they require a lot of time and effort to complete. For example, users can quickly input multiple articles into an AI chat model, ChatGPT, and ask for summaries. This is useful for quick meetings or to get the highlights of multiple articles without having to read them in their totality.

For cybersecurity, this would more closely align with using AI to enhance how logs are interacted with and how the information gleaned from logs is useful to an organization. For example, imagine an organization’s cybersecurity environment where they have multiple pieces of security equipment that all feed their logs into one place. This is traditionally accomplished with a SIEM (Security Information and Event Management). An AI model can be incorporated into the SIEM to recognize very subtle trends and deviations. SIEM developers are using AI to “empower security operations with high-fidelity” detection, response, and threat hunting” (Gurucul, 2024). A premier strength of AI in cybersecurity is crafting it into a companion for human cybersecurity analysts.

Another strength of AI in cybersecurity is using it to improve an organization's cybersecurity posture and help to defend computer assets and equipment. In 2021, the National Security Agency (NSA) stated that AI and ML "will play a role in protecting the United States from malicious cyber actors." AI can make quicker and more factual based decisions than humans (NSA, 2021). Cybersecurity from the home network to national critical infrastructure will benefit from AI integration by making it more efficient and accessible to smaller organizations and individuals.

Weaknesses of AI in Cybersecurity

AI in cybersecurity is not without its weaknesses. One example that has been mentioned is that because AI and ML are developed by humans, who are inherently flawed, we will naturally interject our own biases (Schwartz et al., 2021). As a result, the National Institute of Standards and Technology (NIST) produced a “Special Publication (SP) 1270, Towards a Standard for Identifying and Managing Bias in Artificial Intelligence," to kick start the conversation and concern about bias in AI. We must be very careful to ensure that we do not bring bias into AI because it could lead to inaccurate results, unfairness, and discrimination (NIST, 2023). The responsibility ultimately lays with all of those that have interactions with AI tools. We must work together, from the developer to the consumer, to callout signs of bias that might be harmful to its users.

Another weakness that lies with AI in cybersecurity is its heavy dependence on vast amounts of data. Eugene Dorfman, the Vice President of Engineering at Postindustria Inc., states that when building an ML project, the general rule of thumb is to apply the “10 times rule” (Dorfman, 2022). The 10 times rule states that a model should be trained with 10 times the parameters in the data set (Dorfman, 2022). These parameters are referred to as degrees of freedom. In his article, he explains that an algorithm designed to “distinguish images of cats from images of dogs based on 1,000 parameters,” will need “10,000 pictures to train the model” (Dorfman, 2022).

The issue with AI models like this is that if an organization is not in possession of 10,000 photos or instances of an incoming cyberattack, how do they meet the 10 times rule? The New York Times published an article about how “Tech Giants” like Open AI did just that. In 2021, OpenAI “had exhausted every reservoir of reputable English-language text on the internet as it developed its latest A.I. system” (Metz et al., 2021). The company developed a new tool called “Whisper.” This tool could describe “audio from YouTube videos, yielding new conversational text that would make an A.I. system smarter” (Metz et al., 2021). Google has also used audio from YouTube videos, in the form of transcripts, to feed its AI technology (Metz et al., 2021). Google “may have violated the copyrights of YouTube creators” and Open AI was in a similar boat (Metz et al., 2021). This is where the weakness and dependency of AI become evident. If AI companies do not properly possess of extreme amounts of data, they might turn to unethical means to harvest the data.

One of the greatest weaknesses of AI in cybersecurity space is how malicious users can pose as security researchers to use AI to develop phishing emails and malware. In a presentation from the Office of Information Security and the Health Sector Cybersecurity Coordination Center, “threat actors are using AI for both designing and executing attacks” (2023). ChatGPT is used in the presentation and generates three phishing email templates that could easily be adopted by a hacker, slightly tweaked based on the recipient, and sent to the victim (OIS and HC3, 2023). Chat GPT is also used to produce “malware and launch cyberattacks” within discussion boards on the dark web (OIS and HC3, 2023). Within cybersecurity, being unethically used and swindled into creating malware for the bad guys is AI’s greatest weakness.

Opportunities of AI in Cybersecurity

There are new opportunities available because of AI’s integration into cybersecurity. As previously stated, due to the warnings about bias in AI, there are opportunities for cybersecurity to come together and jointly develop AI models. This is because through a joint development environment, individuals can ensure a common moral ground built into the model. Additionally, this joint development allows developers to work together and gain greater experience. Finally, it facilitates a joint pool of resources for building AI models and a larger workforce working together for a common goal.

The Department of Defense (DoD) has introduced the idea of a joint medical AI capability, across all military branches, to “significantly improve combat casualty care and reduce strategic risk to the joint force” (Donham, 2023). Currently, all branches and research organizations are doing their own development. Therefore, “no single organization is responsible for coordinating and synchronizing this development effort (Donham, 2023). This is preventing a larger body of AI developers from working together on a joint mission for the improved care of DoD wounded. A joint development environment would solve this problem.

Related to community development is the opportunity for collaboration between the private and public sectors. When NIST (National Institute of Standards and Technology) developed the initial draft publications for the “NIST AI Risk Management Framework” (AI RMF 1.0), they asked the public, industry leaders, and academia professionals for their comments. Additionally, the “NIST Trustworthy & Responsible Artificial Intelligence Resource Center (AIRC)” has a free of charge previously mentioned “AI RMF,” “AI RMF Playbook,” glossary, and training. All to further foster a public and private collaboration.

Finally, there are virtually unlimited opportunities for AI integration with cybersecurity. This could be the continued development of preexisting cybersecurity tools or the development of entirely new ones. It could result in sophisticated automated cybersecurity defensive systems that act immediately with very little human intervention or greatly increase the capacity that a single human can do in conjunction with AI. The opportunities for AI and cybersecurity in the future are limitless.

Threats of AI in Cybersecurity

There are threats to AI and its integration into cybersecurity. First, is the intentional or accidental tampering of an AI’s data set, known as data poisoning. It can be intentional where a malicious actor incorrectly modifies data that will be ingested by an AI model so that it does not learn correctly and produces invalid results to queries. It can be accidental where AI developers are unaware that data in the data set is of poor quality and the AI model returns incorrect or biased results. In either case, data ingested by AI must be of the utmost quality to prevent data poisoning.

Another threat to AI is an insider threat. This could be conducted via a data poisoning attack or through deliberately leaking part of the AI’s source code or trained models to the public or competitors. Leaking such proprietary information would cause grave economic damage to the company and take away its competitive advantage.

The final threat to AI is AI itself. Too much reliance on AI by cybersecurity professionals will cause a single point of failure and a fleeting of critical cybersecurity expertise and experience. AI is meant to be a supplemental tool, but never meant to replace human oversight. The cybersecurity industry and professionals must ensure that they always work with AI and do not become replaced by AI.

Applying the “AI RMF”

The “NIST AI RMF,” Risk Management Framework, is a “voluntary resource for organizations designing, developing, deploying, or using AI systems to manage AI risks and promote trustworthy and responsible AI” (2023). The characteristics of the framework to determine a trustworthy AI system include “valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed” (NIST, 2023). AI that is developed without ensuring for the above characteristics can be harmful to both users and communities.

The “AI RMF” can be applied to Chat GPT, an AI chatbot that cybersecurity professionals commonly used for referencing and creating intermediate code. We can apply the NIST trustworthy characteristics to Chat GPT. According to NIST, a safe AI system should “not under defined conditions, lead to a state in which human life, health, property, or the environment is endangered (Source: ISO/IEC TS 5723:2022)” (2023). Unfortunately, with previous builds of ChatGPT, the AI model has led to stolen human property, per the New York Time article (Metz et al., 2024). ChatGPT is violating the trustworthy characteristics suggested by NIST.

The negative actions of OpenAI, the company that makes ChatGPT, can also be applied against the NIST-suggested AI’s trustworthy characteristic of privacy enhancement. Privacy addresses “freedom from intrusion” and “limiting observation” (2023). Unfortunately, OpenAI’s Whisper tool clearly violates the privacy of users and those who did not consent to their information being consumed by OpenAI to develop newer versions of ChatGPT. Therefore, unless the model has received specific updates, it violates the NIST AI trustworthy standard within the “AI RMF.”

Applying the “AI RMF Playbook”

The NIST “AI RMF Playbook” was written as a companion piece to the “AI RMF.” More specifically, the “Playbook provides suggested actions for achieving the outcomes laid out in the AI Risk Management Framework (AI RMF)” (NIST, 2023). It is aligned with each of the four primary AI RMF functions.

The final AI RMF function is “Manage.” “Manage” is defined as how “risks are prioritized and acted upon based on a projected impact” (NIST, 2023). Consider the following scenario: An organization has multiple Microsoft security tools in place, such as Microsoft Defender XDR, Sentinel, etc. Now, they are considering adopting Microsoft Security Copilot, a conversational AI chatbot that can be ingested with information from Microsoft security tools to provide information based on the organization’s security needs. Their biggest concern is ensuring that this new AI tool is managed.

The organization can utilize the “AI RMF Playbook” to manage the AI tool. One suggested action, in the managed function, is to “track and monitor negative risks and benefits throughout the AI system lifecycle including in post-deployment monitoring” (NIST, 2023). The organization should conduct research on Microsoft Security Copilot, or any other AI tool, before adoption. Organizations should also continue to ensure that the tool is managed after adoption by incorporating the system into preexisting change management processes.

Another suggested action is to “regularly assess and document system performance relative to trustworthiness characteristics and trade-offs between negative risks and opportunities” (NIST, 2023). In this scenario, if Microsoft Security Copilot does not present more opportunities than risks, the organization should disassociate themselves from the tool. Management of AI cybersecurity tools is a continuous process to ensure that the tool is doing its job and benefiting the organization.

Conclusion

Cybersecurity’s adoption of AI must remain cautious and calculated. It has the potential to aid us, but it can also do great harm. Like any other advanced technology that came before it, we must come together as cybersecurity professionals to ensure that its continued development is ethical, strong, and produces opportunities for the industry. AI is already here. It is now just a question of how its continued integration will impact cybersecurity, working professionals, and communities.

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