AI and Access Security: How It’s Changing the Industry
Written by:
University of Tulsa
• Dec 22, 2025
AI has advanced to support a wide range of industries, including the security industry. A study from Takepoint Research found that 80% of cybersecurity professionals believe the benefits of AI in access security outweigh the risks, highlighting its potential in the industry. While there are still considerations for integrating AI in cybersecurity systems, strong industry confidence in AI as a tool speaks to its inevitable presence and contributions to the field.
Advances in cybersecurity lead to advances in cyber attacks and create a demand for professionals who can help combat them. Individuals who want to pursue a career in this evolving industry should consider a master’s program in cybersecurity to gain the competencies they need to thrive in the field.
AI and Its Different Capabilities
Artificial intelligence is sorted into three main function-based categories.
Artificial Narrow AI (Narrow AI): This type of AI is considered “narrow” because it can be trained to perform only one task. It cannot perform outside of its designated tasks. Examples of narrow AI include Siri, Amazon’s Alexa, and IBM Watson.
Generative AI (GenAI): GenAI produces images, text, videos, and other forms of data based on user input. It is continuously learning and adapting with the data provided by users. GenAI is capable of retaining information to apply to new tasks, regardless of context, allowing it to perform more complex tasks than narrow AI. Examples of General AI include ChatGPT, Google’s Gemini, and Claude.
Super AI: Super AI is a theoretical form of AI. With super AI, artificial intelligence would surpass human beings’ cognitive functions: learning, reasoning, problem-solving, and emotional intelligence.
What Is Access Control in Security?
Access control plays a critical role in cybersecurity by allowing appropriate network users to access data while protecting it from would-be hackers. Organizations implement access security measures in their network systems to ensure only verified users have access to sensitive information.
Different Types of Access Control
There are four main types of access control in cybersecurity. Each control type enables users to access sensitive information to help protect against different types of hackers and minimize their threat to organizational data.
Role-Based Access Control (RBAC)
With RBAC, granting access to information is based on business function rather than user identity. This type of access control is function-oriented in that it only grants users the data they need to complete a certain task, nothing more.
Discretionary Access Control (DAC)
In a DAC system, individual owners control data sets. Owners are responsible for approving user access to their protected systems at their own discretion.
Attribute-Based Access Control (ABAC)
ABAC relies on a variation of attributes and environmental conditions. For example, a company may utilize ABAC to only allow system access to users during a certain time and at a specific location.
Mandatory Access Control (MAC)
Organizations implement MAC models to categorize users into different clearance levels for varying system access. This approach to scaling access is popular in government and military networks.
Common AI Applications in Access Security
Businesses are applying AI models to reinforce traditional access control methods. There are two main AI applications currently used in access security: biometric authentication and behavioral analysis. These types of AI access security help make the identification process more specific to each user and flag suspicious activities, minimizing cybersecurity risks for companies.
Biometric Authentication
Biometric authentication identifies users by a unique biological trait, making it more difficult for hackers to infiltrate data storage using traditional username and password login credentials. Because this type of AI access security provides more protection, it often replaces the need for a key card or passcode. Common forms of biometric authentication include fingerprint scans and facial recognition.
Behavioral Analysis
Behavioral analysis utilizes AI algorithms to help detect unusual activity within a company’s system network. An example of suspicious behavior that this AI access security method flags is multiple login attempts with invalid credentials.
Detection of unauthorized user attempts is critical to keeping company networks safe. Behavioral analysis keeps IT professionals alert to any cyber threats and identifies potential weak spots within a cybersecurity system.
Ways AI is Changing the Access Security Industry
AI access security is rapidly evolving to ensure companies’ sensitive data remains safeguarded. AI prioritizes automation and efficiency to streamline many processes in access security models. It uses internal, external, decision, and historical data to study patterns and context so it can best protect systems. The following are a few ways AI is changing the access security industry:
Identifying and Monitoring Visitors
Using authentication processes, AI access security allows organizations to identify system visitors, both prior to granting data access and after. Companies also enlist biometric authentication for on-site security through facial recognition and behavioral monitoring.
Integrating Systems
Some AI models can integrate with various security systems, unifying security platforms for more advanced cyber threat monitoring. AI access security’s integration capabilities promote a more comprehensive perspective on network security systems, much to the advantage of cybersecurity teams.
Improving Accuracy
Biometric and behavioral AI tools improve verification accuracy. Biometrics have become a common method in AI access security, as biometric user credentials are much more challenging for hackers to imitate than traditional, non-biometric-based credentials.
Automating Tasks
Another way AI has changed the access security industry is through task automation. The switch to automation for tasks like user enrollment, unusual activity detection, and access-level configuration allows security personnel to reallocate their time and energy to other, non-automated aspects of the security system.
Detecting Threats
AI systems tend to be faster at detecting cyber threats than manual procedures. Quick detection is vital to vulnerable networks, as cyber attacks have the potential to escalate quickly. Detecting suspicious activity with AI reduces potential damage and breaches.
Start Your Career in Cybersecurity at The University of Tulsa
To maintain and guide the evolution of the cybersecurity industry and AI’s involvement, cybersecurity professionals will continue to be needed in the industry. The online M.S. in Cyber Security at The University of Tulsa offers both leadership and technical tracks to foster your academic and career goals.
The program provides students with the knowledge and skills necessary to manage complex security systems and prepare for technological advances to come. With the opportunity to earn a master’s degree in as little as 20 months, even while working full-time, students can advance their careers quickly.
Learn about the TU M.S. in Cyber Security and how it can prepare you for the next step in your cybersecurity career.
Recommended Readings
Do You Need a Degree for a Cybersecurity Career?
Is a Master’s in Cybersecurity Worth It?
Sources:
CIE, “What Is Access Control?”
IBM, “Understanding the Different Types of Artificial Intelligence”
LenelS2, “7 Ways AI is Changing Access Control & Security”
LoginTC, “Types of Authentication Methods”
Microsoft Security, “What Is Access Control?”
PurpleSec, “AI In Cybersecurity: Defending Against the Latest Cyber Threats”
Radiant Logic, “Artificial Intelligence and Identity and Access Management”
TakePoint Research, “AI Integration and Its Outlook in Industrial Cybersecurity”
