MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541122277 A) filed by Malla Reddy (MR) Deemed to be University; Malla Reddy University; Malla Reddy Engineering College For Women; Malla Reddy College Of Engineering And Technology; and Malla Reddy Vishwavidyapeeth, Malkajgiri, Telangana, on Dec. 5, 2025, for 'ai-driven trust score mechanism for decentralized authentication.'
Inventor(s) include Venkataranama Musala; Dr. G. Latha; Mrs. Busani Sravani; Dr A Nagaraju; and Shastri Vishwashree.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "The current invention has revealed an AI-based Traveling Score Mechanism (ATSM) of decentralized authentication systems, intended to substitute stringent and binary access controls (pass/fail) with a perpetually reconsidered and probabilistic trust rating. Traditional decentralized and distributed authentication schemes, including those based on zero-knowledge proving or simple multi-factor authentication, can only authenticate fixed credentials or discrete authentication interactions. This method also has weaknesses to advanced attacks such as session hijacking, man-in-the-middle attacks, and credential stuffing since it does not constantly evaluate the contextual reliability and the danger of the behavior of the authenticated party in real-time. The ATSM bypasses this static weakness through the introduction of risk-based access control which is dynamic. The main part of the invention is the Behavioral Anomaly Detection (BAD) Engine. It is an engine whose special Recurrent Neural Network (RNN) or Transformer Model processes continuous multi-source and non-static streams of data related to an authenticated user or device. These streams are data provenance lineage, geographic velocity, time-of-day access patterns, resource consumption measurements and sequence of the access request. The BAD Engine receives a profile of the normal operation of the entity, and produces a continuous Trust Risk Vector (TRV), indicating the deviation of the current activity form the projected, secure baseline. The TRV is sent to the Decentralized Trust Score Regulator (DTSR) and is converted to a single, scalar Trust Score (TS) which is usually 0.0 (High Risk) to 1.0 (Trusted) in value. This computation is carried out on a local level and certified by an authenticated consensus protocol in the decentralized network. Adaptive Access Policy (AAP) Module then uses the TS to regulate the privileges of the entity. In contrast to binary systems, the AAP can actively apply fractions of restrictions: when a low TS is reached, a re-authentication may be forced, high-value data may be restricted or rate limits may be placed on transactions, without necessarily booting the user. It is an AI-driven adaptive mechanism, which guarantees a constant proportionality of security to risk. The ATSM ensures a very robust and resource-efficient security layer by decentralizing and operating zero-trust architectures with the goal of reducing the window of opportunity of compromise to a minimum, relocating authentication to an ongoing and probabilistic service."
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