AI Detection & Intelligence System for Commercial Operations​

PureCipher has conducted a comprehensive study and engaged in a series of in-depth conversations with experts to address a critical issue: the rapid detection and effective identification of potential threats and irregularities in various commercial settings. The current methods, which rely heavily on manual inspections, are time-consuming, demand specialized expertise, and are often imperfect. This inefficiency puts businesses at risk and slows down operations.
PureCipher’s AI Detection and Intelligence System (AI-DIS) aims to address this problem. AI-DIS leverages Natural Language Processing (NLP) and Vision Transformer/Convolutional Neural Network (CNN) technologies, pre-trained to handle a wide range of queries with high universality and generality. Using advanced proprietary techniques and AI models, AI-DIS tracks known entities, associations, and similarities through re-identification. Essentially, AI-DIS functions as a sophisticated multimodal AI system, enabling identification, re-identification, knowledge graph creation, tracking, and visualization.
The system enables operators to automatically scan and detect potential risks, significantly enhancing both the speed and accuracy of threat detection and providing actionable guidance to staff. Re-identification capabilities ensure that once an irregularity is detected and cataloged, it can be tracked continuously, even if it appears in different forms or contexts.
Finally, all transmission is conducted using PureCipher’s Noise Based Communications (NBC) technology. This technology maps data to randomly generated noise vectors, which are then transmitted. The core innovation is that the randomly generated noise itself has absolutely nothing to do with the actual data; it is entirely unrelated. NBC does not resemble conventional encryption/decryption methods but is backward compatible, allowing messages to be encrypted before the mapping process if needed.

PureCipher's AI-DIS system simplifies the risk detection process as follows:

Field Operators: Operators, often overwhelmed with sensory inputs, are equipped with a camera, drone, or surveillance equipment capable of capturing the environment.
Real-time Analysis: The camera feed is streamed to our pre-trained AI-DIS model, which can be configured on-the-fly based on user-defined queries.
Dynamic Detection: The operator inputs specific risks or irregularities they need to detect (e.g., theft, unauthorized access, abnormal behavior). The AI-DIS system analyzes the camera feed in real-time, identifying and flagging the specified objects.
Reidentification: Once an irregularity is detected and cataloged, the re-identification capability ensures that it can be tracked continuously, even if it reappears in different forms or contexts.
Secure Transmission: All data transmission is conducted using PureCipher’s Noise Based Communications (NBC) technology, which maps data to randomly generated noise vectors for highly secure and virtually unhackable communication.
Actionable Guidance: The system can provide immediate notifications to the operator, highlighting the detected irregularities. Additionally, PureCipher can collaborate with clients to integrate detailed instructions on handling detected objects, further enhancing the system’s utility.

Commercial Applications:

Theft Detection: AI-DIS can identify and flag suspicious activities indicative of theft, such as unusual movements or behaviors in retail environments. This enables real-time alerts to security personnel for immediate action.
Unauthorized Access: The system can monitor access points and detect unauthorized entries, ensuring that only authorized personnel are within restricted areas.
Abnormal Behavior: AI-DIS can analyze behaviors in real-time to detect abnormalities, such as loitering, sudden rushes, or other unusual patterns that may indicate a security concern or emergency.
Operational Disruptions: By continuously monitoring equipment and operational processes, AI-DIS can detect early signs of malfunctions or disruptions, allowing for timely maintenance and minimizing downtime.
Environmental Changes: The system can track changes in the environment, such as new constructions, obstacles, or modifications in layout, to ensure safety and compliance with regulations.
While we currently focus on detecting anomalies indicative of security breaches and operational risks, ensuring early detection and prevention, we plan to extend AI-DIS in the future to utilize images and video feeds to detect and analyze a wide range of activities. This includes monitoring customer behavior for business intelligence, tracking asset movements, and analyzing workspace utilization. With a view to the future, this system will significantly enhance situational awareness and decision-making capabilities for businesses across various sectors.
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John C. Carroll

With over 30 years of experience and a deep industry background spanning multiple markets, John is a seasoned sales professional. He boasts a successful track record with prestigious organizations such as IBM, Raytheon, Optiv, Automatic Data Processing, Skybox, and Core Security. John's expertise extends to advising several cybersecurity startups and consulting within the intelligence community for companies like M20 and GSS, primarily focusing on the Department of Defense (DoD) and Government sectors. A graduate of the Solution and Strategic Selling programs, John has not only completed these rigorous courses but has also taught them, further cementing his sales expertise. He holds a degree in Business Administration and Environmental Science from Ramapo College of New Jersey, where he is honored as a member of the Football Athletic Hall of Fame.

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Dr. Brandon Langenberg

Brandon received his PhD in mathematics from Florida Atlantic University with a focus on quantum cryptanalysis. His research included an AFRL research grant to study quantum resource estimations on symmetric cryptography and quantum computing. Brandon has worked as a senior researcher and the principal investigator on post-quantum cryptographic algorithms working with engineering team for implementations of quantum-safe cryptographic solutions that will withstand post-quantum cyber-attacks. Brandon is currently leveraging his cryptographic background to build a quantum resistant Fully Homomorphic Encryption solution.

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Graham Morehead

Graham teaches AI/ML at Gonzaga University. For over 25 years, Graham has been developing cutting-edge technologies across a wide array of disciplines, from speech recognition to physics, and national security. His research into Complex Adaptive Systems led to several TEDx talks related to ecological and human modeling. His other work addresses real estate prediction systems and a natural language understanding platform that solves some of the problems with GPT. This work was pursued both for the private sector and government agencies. He holds hardware and software patents related to high-performance computing and natural language.

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Dr. Matt Ikle

Dr. Ikle’s specialties include neuro-symbolic artificial intelligence, probabilistic logic, evolutionary computation, mathematical modeling and simulation, bioinformatics, quantitative finance, and nonlinear and complex dynamical systems. Dr. Ikle also services as the Chief Science Officer at SingularitityNET, a strategic collaborator of PureCipher. Prior to PureCipher, Dr. Iklé was a tenured full Professor of Computer Science and Mathematics at Adams State University, where he obtained numerous government grants to engage students in both his AI and mathematical modeling and simulation research. He has held faculty positions at the University of Texas, the University of Nevada, and Xiamen University in China, as well as an array of leadership, consulting, and research roles within industry. He earned his doctorate in Mathematics, with a minor in Physics, in 1993, from the University of Wisconsin at Madison.

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Dr. William Hahn

Dr. William Edward Hahn was a tenured Assistant Professor of Mathematics at Florida Atlantic University and director of the Center for Future Mind’s AI Research Initiative. He also founded the Machine Perception and Cognitive Robotics (MPCP) Laboratory, where he oversaw research teams working on cryptographic computing and artificial intelligence. Dr. Hahn has been deeply involved in the development of private and secure AI systems involving sophisticated techniques such as Fully Homomorphic Encryption (FHE) and Secure Multiparty Computation (SMPC). Dr. Hahn received his PhD from Florida Atlantic University for his work in Sparse Coding and Compressed Sensing.

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Wendy Chin

Wendy is a senior technology executive with global operations experience heading divisions within Fortune 100 companies (Pfizer, AT&T, Siemens) and start-ups in cybersecurity, artificial intelligence, and health informatics. She is a recognized Thought Leader and Speaker enabling businesses to establish a strong commercial market position through product strategy, roadmap design, distribution channel development, and go-to market execution. She has been deeply involved in innovative product initiatives that include Cyber Security and Data Encryption Solutions, Optical/Robotics Systems, AI/ML & NLP, Health Informatics, and even her own ice cream brand. She holds an MBA from The Wharton School along with Master’s and Bachelor’s degrees in Electrical Engineering from Cornell University.

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