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Generative AI and the future of physical security

Generative AI and the future of physical security
This article will examine the transformative impact of generative AI on physical security and the ways it can affect businesses in the future.
Integrating generative AI into physical security systems is more than just an enhancement—it is poised to transform the entire sector. With generative AI’s ability to create new, synthetic data models that predict and simulate security threats, many experts are eyeing this powerful new tool to achieve new security levels.

This article will examine the transformative impact of generative AI on physical security and the ways it can affect businesses in the future. 

What is Generative AI?

Generative AI is a type of artificial intelligence whose models create new content such as text, video or images. Whereas traditional AI focuses more narrowly on analyzing specific datasets and identifying patterns to make predictions, generative AI produces new data based on learned patterns from a wide swath of data sources. Think of DALL-E producing new images of famous actors or chatbots engaging in customer service dialogue.

Generative AI and the Physical Security Industry

Most security experts are aware of the current uses of predictive AI in security—facial recognition, video analytics and access control systems. Yet generative AI really signifies a paradigm shift in these tools. Generative AI has the potential to make an impact in several physical security applications:

A. Biometrics

Biometrics has been a long-established security tool in corporate and governmental organizations. Combining generative AI with biometric data can produce more accurate identification and threat assessment.

For example, generative AI can create synthetic images of faces to augment training datasets, helping to improve the accuracy and robustness of facial recognition models. It can generate diverse facial expressions, lighting conditions, and angles of existing biometric data, which can then be used to train facial recognition systems to better recognize faces under various conditions.

In addition, generative AI can aid in detecting deepfake imagery. AI-generated video and images present a major threat to security and identity theft. However, a recent study from Drexel University used generative AI training models to analyze data down to the pixel level. These sophisticated AI models were able to discern patterns that indicate computer generation, producing a 95–98% accuracy in deepfake detection.

B. Video Surveillance Analytics

Video surveillance tools are bound to see the most exciting improvements from generative AI. The ability to generate its own datasets for training can be deployed for a wide set of behavioral predictions.

For instance, generative AI can analyze video feeds to identify and track the movement and density of people for crowd detection. By generating predictive models on its own, it can anticipate crowd behavior and detect unusual patterns to ensure public safety during events like protests or large gatherings.

Moreover, generative AI can be used for other modes of behavior detections such as examining human gestures or a speech analysis from various sources of data. This could help to identify and interpret suspicious human actions in real time, providing immediate alerts for security enhancement and situational awareness.

C. Access Control

Access control systems are also set to get an overhaul with generative AI. With the system’s ability to train itself on simulated datasets, this adds extra layers of security in the authentication process.

Generative AI can analyze an individual’s patterns of behavior and access entries over time to establish normal usage patterns. This will help with anomaly detection, indicating unusual access attempts or behavior deviations. By continuously learning from data and generating predictive models, the system can identify suspicious behaviors or access patterns that deviate from expected norms.

Access control systems can also draw on generative AI and predictive analytics to enhance physical security or optimize resources. AI models can predict peak access times, crowd movements, and security incidents based on historical data and event schedules. This means the system can optimize personnel deployment and adjust surveillance camera coverage dynamically, ensuring effective monitoring and response capabilities during high-traffic periods or special events.

Future Outlook of Generative AI Security

Generative AI is a cutting-edge form of artificial intelligence that has the ability to transform physical security from a passive monitoring tool into a mighty proactive engine. By harnessing its unique characteristics of dataset creation and predictive modeling, current tools like biometrics, video analytics and access control can have a more nuanced and foresighted approach to surveillance.


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