7 Ways AI is Changing Access Control & Security
In a rapidly evolving threat landscape, access control must adapt to new challenges. AI security technologies are a game-changer, introducing innovative ways to enhance access control, streamline security tasks, and redefine the user experience. In this blog post, we'll explore the seven key ways AI is revolutionizing access control and physical security as well as the types of AI seen today.
What Types of AI are Used in Access Control?
There are several branches of AI used in access control today. These include:
- Natural Language Processing (NLP): NLP analyzes text and speech to identify potential threats. For example, NLP can help identify keywords or phrases that are associated with fraud or malicious activity. NLP can also be used to help Security Operators and Installers get custom help and instructions to perform their jobs more effectively.
- Machine Learning (ML): Machine learning can be used to create trained models to identify patterns and non-obvious relationships in massive amounts of data. These models can then be used to make predictions about potential threats, impending device failures, handling of alarms and a variety of other things. For example, ML can be used to verify access control policies to ensure they are clear and effective. It can also be used to vet people to ensure they comply with policies and have the proper credentials to perform assigned tasks.
But beyond the types of AI used for access control, it’s important to also look into the current landscape, as new technologies and capabilities develop.
What Does the Current Access Control Landscape Look Like?
Traditional access control systems, such as role-based access control (RBAC), attribute-based access control (ABAC), policy-based access control (PBAC), and legacy physical access control (PACS) are often time-consuming, error-prone, and inflexible. They require security personnel to manually create and maintain roles, permissions, policies, and infrastructure, as well as monitor ongoing access requests. Additionally, traditional access control systems can be easily exploited by bad actors through social engineering, tailgating, or other vulnerabilities (like inconsistent policies). As organizations scale, these challenges can become even more pronounced, which presents more opportunities to use AI in physical security.
How is AI Used in Security?
AI is rapidly transforming the security landscape. Security personnel can use AI-powered solutions to identify, prevent, and address security threats more effectively than ever before, reducing their workload while improving accuracy.
The two key applications for AI in access control include:
Biometric Authentication
AI-powered systems increasingly use biometric data such as fingerprints, iris scans, and facial recognition to accurately verify an individual's identity. Biometric authentication is often more secure than traditional methods, such as key cards or passwords, because it is difficult to spoof. It can also be used with card and mobile credentials to allow for multi-factor authentication.
Behavioral Analysis
Behavioral analysis leverages AI algorithms to identify unusual or suspicious activities, like trying to access a system or network with incorrect credentials multiple times. Behavioral analytics also enhances security to help detect unauthorized access attempts, even if the individual is using a valid biometric credential.
Some common use cases for these AI-powered solutions include automated incident response, more accurate risk assessment, compliance monitoring, threat intelligence information gathering, and enhanced security orchestration, automation, and response (SOAR). AI systems can also learn over time, as more historical data is generated they can perfect models which make these solutions more accurate and precise over time. As AI technology continues to develop, we can expect to see even more innovative and effective ways to enhance an organization’s security posture. Thus making AI play an evolving important role in access control.
Why is AI Important for Access Control?
AI significantly enhances access control by proactively identifying threats, simplifying system management, and streamlining access control processes. AI-powered access control systems can integrate with other security systems, like video surveillance systems, to enable a more comprehensive approach to security. Ultimately, AI is helping organizations better protect their data and assets from unauthorized access and malicious activity. AI can also be connected with massive amounts of external and publicly available data to identify security threats. For example, a person who is requesting access may have had restraining order issues that are not known by the access control system directly. That being said, AI isn’t a perfect solution when it comes to access control.
What are the Current Limitations of AI in Access Control?
While AI has immense potential, it also has limitations. AI-powered access control systems require large amounts of data to train and operate effectively. This data can be expensive and time-consuming to collect, and it may not always be available. When used in conjunction with cloud-based access control systems, the burden of data usage can be shared with providers like LenelS2. It can also ease access to external data sources that may be valuable. For reasons like that, many companies have begun to look into how they can integrate cloud-based access control for their buildings.
Additionally, these systems are not perfect. They can provide more insights but may need human verification for some recommendations. If the data inputs are not well-represented or the environment is “noisy” or invalid data is ingested, the system may reject actual users or accept bad actors. Any AI solution will have some level of false positives and false negatives. Finally, many AI-powered systems lack transparency, so it can be difficult to understand how they make decisions. Therefore it is important to integrate AI solutions that can show how decisions were made so that corrections or adjustments can be made. Regardless, in today’s current landscape, AI is changing how we look at access control and security overall. It can find threats and concerning anomalies in massive amounts of data which would be almost impossible to find through pure manual analysis.
What are 7 Ways AI is Changing Access Control?
AI is fundamentally changing access control by introducing automation, intelligence, and efficiency, making access control more accurate and real-time, while streamlining time-consuming tasks. AI can use internal and external data, raw historical data, and decision data to better understand patterns and context. Here are some of the key ways AI is enhancing access control:
1. Data Tracking & Analytics
AI tracks and analyzes access data to provide insights into user behavior, entry patterns, and security risks. This can be combined with external data sources. This information can be used to identify potential threats and escalate them for investigation, for example, if an employee is accessing the building at unusual times or has been restricted due to changes in role or even external court orders or affiliations with competitors which can make that person a threat. Or it can also determine if that person's actions could result in a company, government or certification violation. For example, a person gaining access to handle a maintenance issue who does not have the proper prerequisite certifications and approvals could pose a personal threat to the individual and result in a violation or fine.
2. Access Accuracy
AI-powered biometric authentication methods can offer higher accuracy than single-factor traditional access control systems. Combining biometrics with other forms of identity such as card or mobile credentials can provide higher accuracy and determine that the person gaining access is not using someone else’s credentials. This is because AI and Deep Learning techniques can recognize patterns and identify individuals more reliably than other non-biometric-based credentials.
3. Decision Making
Machine Learning techniques and tools can process and analyze large amounts of data internal and external, to identify patterns and trends and non-obvious associations. AI-powered solutions can automatically generate interactive relationship reports and recommendations which can be used to make better decisions from tactical activities, such as identifying and rejecting fraudulent access requests, resolving identity conflicts i.e. someone who may have multiple name variations. It can also be used to identify vulnerabilities and recommend solutions before the vulnerabilities are exploited.
4. Visitor Monitoring
Behavioral analysis combined with authentication data can be used to identify and monitor visitors preventing unauthorized access and potential physical security threats. For example, AI can analyze video in near real-time and alert security personnel if anomalous behavior is detected that may be of concern. It can also detect events such as tailgating, loitering, threatening behavior and many more. Learning systems can even be trained to detect specific behaviors that may be unique to their facility or industry. Combining events that are detected from video with access data, identity data and historical data can give security operators more insight and allow them to manage more locations with few resources.
5. Task Automation & Recommendations
A significant way AI is improving security solutions is through the automation of tasks and recommendations on the next best action to be taken. Tasks like user enrollment, access level configuration, nuance alarm management and permissions management can be automated allowing security operators and administrators to use their precious time more effectively. AI can also make recommendations and detect threats and potential vulnerabilities.
In addition to reducing manual errors, task automation and recommendations can free up security personnel for more important work such as responding to security incidents. This area of AI can also be predictive in nature giving indications where there are vulnerabilities, devices that are ready to fail, security and safety exposures. By identifying these types of concerns before they are exploited action can be taken to prevent injury, handle exposures, and fix devices before malicious incidents happen.
6. Integration Capabilities
Certain AI systems can integrate with various security systems — like video surveillance systems and intrusion detection systems — to provide a unified platform for enhanced monitoring and control. This can help to provide a more comprehensive view of the security environment and improve security’s ability to respond to threats.
7. AI Alarm Systems
AI-powered alarm systems can better detect unauthorized access, intrusion attempts, nuance alarms, and other security threats allowing operators to focus on what is important. By preventing intrusion or stopping intruders in their tracks operators can help protect critical assets and intellectual property. This information can help security personnel generate alerts and take corrective action in near real-time.
What are the Challenges of Using AI for Access Control?
Despite its benefits, AI in access control comes with its share of challenges. Organizations must address privacy concerns and potential bias while ensuring transparency in their AI implementations, and ensuring they are following the best practices to prevent cyber physical attacks. Additionally, AI keeps evolving at a breakneck pace, so it can be difficult (and expensive) for organizations to maintain and update homegrown systems. That is why many organizations look to a provider such as LenelS2 who integrates with industry-leading AI engines to meet physical security needs.
Advancing Access Control Using AI with LenelS2
As the global leader in advanced physical security solutions, we at LenelS2 have been named a global leader in access control. LenelS2 has a proven track record of success in helping organizations implement and manage AI-powered security solutions along with partners who are proven leaders in AI tools such as generative AI, natural language processing, vision analytics, and AI link analysis.
Please note, the material provided is for informational purposes only, is general in nature, and is not intended to and should not be relied upon or construed as professional, medical or legal advice. Individuals and entities using or referencing the materials are encouraged to consult a professional regarding any specific circumstance. LenelS2 expressly disclaims all responsibility and shall have no liability for any damages, injuries of any kind or any liability whatsoever suffered as a result of your reliance on the information set forth in these materials.
