How to stop tailgating in its tracks with intelligent access control
For businesses and organizations worldwide, particularly those with high security demands such as financial institutions, government agencies and large corporations, securing entry points is paramount. Access control systems serve as a first line of defense. By verifying the entry of employees and visitors to the premises, they help protect both sensitive information and physical assets. However, traditional systems often leave a critical security gap, overlooking such events as tailgating.
Tailgating is a specific security breach where an unauthorized individual gains access to a secure area by following closely behind an authorized person.
Although it may seem harmless, tailgating poses serious threats. According to IBM’s Cost of Data Breach Report 2024, malicious insider attacks resulted in the highest costs, averaging $4.99 million, including by social engineering means and compromised credentials being the top initial attack vector.
For industries like data centers, the value of protected information can reach billions of dollars. Therefore, a single breach caused by tailgating could lead to catastrophic financial and reputational losses. The global average cost of a data breach rose by 10% over the past year, reaching $4.88 million, according to IBM’s 2024 report. The result is substantial financial penalties and damage to customer and stakeholder trust. In addition to jeopardizing customers’ sensitive personally identifiable information (PII), tailgating poses significant physical security risks, including theft, vandalism and potential harm to personnel.
Factors contributing to tailgating
The risk of tailgating stems from the combination of human error and gaps in traditional security measures. One common scenario involves exploiting an authorized individual’s trust or lack of awareness — such as slipping through a door behind someone with a valid badge or keycard. If access control systems are outdated or poorly implemented, they often fail to detect and prevent unauthorized entry. Moreover, when security personnel do not follow proper protocols, such as closely monitoring access points and verifying the credentials of individuals before granting them access, it creates opportunities for tailgating attacks to occur.
Facial recognition technology can reduce the risk of tailgating by adding an additional layer of authentication when a tailgater is trying to enter the premises. Surveillance cameras are effective for recording events but lack real-time detecting capabilities unless combined with security artificial intelligence (AI). They require manual review, which is time-consuming and reactive rather than proactive. Human operators are unable to maintain round-the-clock vigilance, and as a result, the chances of unnoticed breaches increase.
Large organizations with high foot traffic face additional challenges, as the sheer volume of people entering and exiting makes it difficult to track unauthorized access effectively. Without a proactive approach that combines robust access control with intelligent, real-time anomaly detection, these vulnerabilities persist and remain exploitable.
The Role of AI-enabled video analytics in combatting tailgating
Mitigating the risk of tailgating requires a multi-faceted approach, integrating technology with strategic human resource management. One of the emerging solutions is the use of AI-enabled video analytics. These systems continuously monitor access points and analyze video feeds for specific patterns. Leveraging machine learning algorithms and computer vision, they automatically detect behaviors consistent with tailgating, such as multiple individuals entering through a single-secured entry point, and immediately alert security personnel to unauthorized entries. Here is where the main advantage of AI-enabled access control lies.
Unlike traditional systems, AI- enabled solutions detect tailgating attempts in real time by identifying unauthorized and unknown persons entering your premises. There are a few solutions capable of running cutting-edge auto-enrollment on the go, whereby each person is automatically added to the database and verified against their credentials and permissions. In case an unknown person is detected, the system instantly issues alerts for security personnel to take appropriate action. Unlike traditional watchlist technologies designed to look for “be on the lookout” (BOLO) individuals, auto-enrollment tech can pick up unknown persons and alert to them in real time.
Below are some more benefits of leveraging AI-enabled video analytics:
Seamless integration with access control systems — AI-enabled video analytics can be seamlessly integrated with existing access control systems. Working in conjunction, these systems not only alert security personnel in real-time about potential breaches but also take immediate action, such as automatically locking doors and activating sound alarms. Such multi-tiered approach enables organizations minimize the risk of unauthorized access and create intelligent environment where physical security dynamically responds to threats.
Enhancing security with biometric technologies — Biometric technologies address the critical weakness in traditional access systems which often depend on keycards, PIN codes or credentials that can be compromised, stolen, shared or duplicated. Facial recognition solutions verify identities based on unique, non-transferable facial traits and cross-check them against a secure, pre-approved database in real time. This approach minimizes human error and guarantees that only authorized individuals can enter restricted or sensitive areas, significantly enhancing overall security.
Generating actionable security insights — By integrating advanced machine learning algorithms and computer vision, AI systems go beyond mere detection of unauthorized entries. They generate actionable insights that enable organizations to optimize their overall security strategy, reinforce vulnerable areas and allocate resources more effectively.
Scalability and future-proof solutions — Scalability and cost-effectiveness make these systems feasible solutions for growing organizations, as they can seamlessly adapt to increased security demands without requiring substantial infrastructure overhauls, thereby providing a future-proof investment. They can be deployed across multiple locations, ensuring consistent and centralized security monitoring.
Improved accuracy and reliability — Machine learning algorithms improve over time by learning from historical data and evolving patterns. This allows them to accurately distinguish between normal and anomalous behaviors, which significantly reduces false positive alerts and increases reliability. This ensures that instances where unauthorized individuals attempt to piggyback are not missed, reduces unnecessary disruptions and builds trust in the system.
In addition to implementing advanced technological solutions, it is important to foster a culture of security awareness within an organization. Employees should be aware of the risks of tailgating and be able to identify and respond to such attempts appropriately. When staff members are actively engaged in maintaining security, they complement the capabilities of automated systems ensuring a more comprehensive approach to preventing unauthorized access.
One more key takeaway
Tailgating remains a significant threat. Apart from jeopardizing sensitive information, tailgating poses significant risk to physical security, compromises employee safety and potentially damages organizational reputation. However, AI-enabled video analytics offers a forward-thinking solution that bridges the gaps left by traditional security measures. By proactively detecting and preventing unauthorized access, these systems enhance organizational security, safeguard sensitive assets and ensure compliance with regulatory standards. Combined with rigorous employee training and updated policies, the adoption of advanced AI technologies can help businesses mitigate these threats and create a safer, more secure environment.
