The National Intelligence Grid or NATGRID is an integrated intelligence master database structure for counter-terrorism purposes which connects databases of various core security agencies under the Government of India. It collects and analyses comprehensive patterns procured from 21 different organizations that can be readily accessed by security agencies round the clock. As of September 2025 its CEO is Hirdesh Kumar. NATGRID came into existence after the 2008 Mumbai attacks. The Government of India in July 2016 appointed Ashok Patnaik as the Chief Executive Officer (CEO) of NATGRID. The appointment is being seen as the government's effort to revive the project. Patnaik's appointment was valid till 31 December 2018. As of 2019, NATGRID is headed by an Indian Police Service (IPS) officer Ashish Gupta. The Ministry of Home Affairs on 5 February 2020 announced in Parliament that Project NATGRID with all its required physical infrastructures been completed as of 31 March 2020 and the NATGRID solution went live as of 31 December 2020. == Reason for establishment == The landscape of Terrorism in India and the subsequent response by Law enforcement in India have necessitated a sophisticated data-integration framework, positioning NATGRID as a vital tool for national security agencies. This shift towards Mass surveillance in India is rooted in a broader policy evolution of state monitoring, which is technologically enabled by the India Stack—the foundational digital infrastructure providing the API-based backbone for government service delivery and identity verification. This ecosystem is further bolstered by advanced Signal intelligence capabilities and the implementation of SIM binding, a security protocol that anchors a user’s digital identity to a specific mobile device and verified SIM card to prevent identity fraud and unauthorized access. Collectively, these elements form a 360-degree surveillance and authentication grid designed to preemptively identify threats by synthesizing historical, financial, and real-time communication data across disparate platforms. === Terror attacks in India === The 2008 Mumbai attacks led to the exposure of several weaknesses in India's intelligence gathering and action networks. NATGRID is part of the radical overhaul of the security and intelligence apparatuses of India that was mooted by the then Home Minister P. Chidambaram in 2009. The National Investigation Agency (NIA) and the National Counter Terrorism Centre (NCTC) are two organisations established in the aftermath of the Mumbai attacks of 2008. Before the Mumbai attacks, a Pakistani origin American Lashkar-e-Taiba (LeT) operative David Coleman Headley had visited India several times and done a recce of the places that came under attack on 26/11. Despite having travelled to India several times and having returned to the US through Pakistan or West Asia, his trips failed to raise the suspicion of Indian agencies as they lacked a system that could reveal a pattern in his unusual travel itineraries and trips to the country. It was argued that if they had a system like the NATGRID in place, Headley would have been apprehended well before the attacks. === Need for the integrated intelligence system === During the inauguration of NATGRID campus in Bengaluru, the Minister of Home Affairs, Amit Shah stated that a new national database is in the process of being made which will bring a change in the current ways of functioning of agencies once it's ready also adding that the government has entrusted the task of developing and operating a state-of-the-art and innovative technology system. It is accessible to 11 central agencies in the first phase and in later phases will be made accessible to police of all States and Union Territories and only authorized personnel are allowed access to the platform on a case-to-case basis for investigations into suspected cases of terrorism. NATGRID has a total fund allocation of ₹3,400 crore (US$355 million). d == Legal framework == Relevant legal framework: Digital Personal Data Protection Act, 2023 – The legislative framework governing how digital data is handled. Information Technology Act - Interception Rules, 2002 – The specific regulations under the Information Technology Act that govern these agencies. National Security Act of 1980, evidence-based preventative detention of suspects Right to Information Act, 2005, for obtaining information from the government and used by activists and whistleblowers == Structure and functions == === Multi-agency integrated intelligence database === NATGRID is an intelligence sharing network that collates data from the standalone databases of the various agencies and ministries of the Indian government. It is a counter terrorism measure that collects and collates a host of information from government databases including tax and bank account details, credit/debit card transactions, visa and immigration records and itineraries of rail and air travel. It also has access to the Crime and Criminal Tracking Network and Systems, a database that links crime information, including First Information Reports, across 14,000 police stations in India. This combined data will be made available to 11 central agencies, which are: the Research and Analysis Wing (R&AW), Intelligence Bureau (IB), National Investigation Agency (NIA), Central Bureau of Investigation (CBI), Narcotics Control Bureau (NCB), Financial Intelligence Unit (India) (FIU), Enforcement Directorate (ED), Central Board of Direct Taxes (CBDT), Central Board of Indirect Taxes and Customs (CBIC), Directorate of Revenue Intelligence (DRI) and Directorate General of GST Intelligence. Also as stated by the MHA, NATGRID will have an in-built mechanism for continuous upgradation. In the later phases of NATGRID integration, the central government further plans to integrate 950 additional organizations into it. === Key components and users === ==== Some important backend data feeds to the NATGRID (middleware) ==== National Crime Records Bureau's Crime and Criminal Tracking Network and Systems (CCTNS) national-integrated law-and-order database for the state-level police forces: CCTNS is a mission-mode project under the National e-Governance Plan that interconnects over 15,000 police stations across India. It serves as the primary source for NATGRID to access digitized FIR (First Information Report) data and criminal history records from state-level law enforcement. NSA's National Technical Research Organisation (NTRO) national security-based database feed to NATGRID: NTRO serves as a primary technical data provider to NATGRID, offering specialized intercepts and satellite imagery. While NATGRID functions as a centralized data-integration middleware under the Ministry of Home Affairs, NTRO reports to the National Security Advisor within the Prime Minister's Office. DRDO's NETRA (Network Traffic Analysis) ELINT-based mass surveillance system for monitor internal internet traffic for keywords related to terrorism and criminal activity within Indian borders: Developed by the Centre for Artificial Intelligence and Robotics (CAIR), NETRA is an internet monitoring system capable of scanning traffic for specific trigger words. It provides digital behavioral triggers that NATGRID can cross-reference against structural data like financial or travel records. NETRA is a massive software network used to intercept and analyze internet traffic (emails, social media, blogs) for keywords like "bomb," "attack," or "kill." The intelligence gathered by NETRA regarding suspicious digital patterns or "keyword hits" can be fed into NATGRID. This allows an investigator to see if a person flagged by NETRA also has suspicious travel (from airline databases) or financial records (from bank databases) linked within NATGRID. Department of Telecommunications (DoT's Central Monitoring System (CMS) for lawfully intercepting national and international telecomm data: CMS is the centralized system for lawful interception of all telecommunications (phone calls, SMS, and data) in India, managed by the Department of Telecommunications (DoT). While CMS focuses on the content and metadata of real-time communication, NATGRID focuses on historical/structural data (tax, travel, identity). They represent two halves of a 360-degree surveillance profile: CMS listens to what a suspect says, while NATGRID tracks where they go and what they own. The CMS allows for the lawful interception of telecommunications metadata and content in real-time. In the broader surveillance architecture, CMS provides the "active" communication profile while NATGRID provides the "static" historical profile. Telecom Enforcement Resource and Monitoring (TERM) - Telecomm Regulatory & Verification Node for telecomm KYC: TERM cells verify subscriber identity (KYC) and maintain the integrity of telecom databases. NATGRID relies on these audited records to ensure the accuracy of telephone-to-identity mapping. TERM
Manual override
A manual override (MO) or manual analog override (MAO) is a mechanism where control is taken from an automated system and given to the user. For example, a manual override in photography refers to the ability for the human photographer to turn off the automatic aperture sizing, automatic focusing, or any other automated system on the camera. Some manual overrides can be used to veto an automated system's judgment when the system is in error. An example of this is a printer's ink level detection: in one case, a researcher found that when he overrode the system, up to 38% more pages could be printed at good quality by the printer than the automated system would have allowed. Automated systems are becoming increasingly common and integrated into everyday objects such as automobiles and domestic appliances. This development of ubiquitous computing raises general issues of policy and law about the need for manual overrides for matters of great importance such as life-threatening situations and major economic decisions. The loyalty of such autonomous devices then becomes an issue. If they follow rules installed by the manufacturer or required by law and refuse to cede control in some situations then the owners of the devices may feel disempowered, alienated and lacking true ownership. == Major incidents == China Airlines Flight 140 crashed, causing many deaths, due to a misunderstanding about the manual overrides for the autopilot. The Take-Off/Go Around system had been activated to abort a landing. It was programmed to ignore manual controls in this situation but the human pilots tried to continue the landing. The conflicting control signals from the pilots and autopilot then resulted in the aircraft stalling and crashing. The autopilot for this aircraft type was then reprogrammed so that it would never ignore a manual override.
Ross Quinlan
John Ross Quinlan is a computer science researcher in data mining and decision theory. He has contributed extensively to the development of decision tree algorithms, including inventing the canonical C4.5 and ID3 algorithms. He also contributed to early ILP literature with First Order Inductive Learner (FOIL). He is currently running the company RuleQuest Research which he founded in 1997. == Education == He received his BSc degree in Physics and Computing from the University of Sydney in 1965 and his computer science doctorate at the University of Washington in 1968. He has held positions at the University of New South Wales, University of Sydney, University of Technology Sydney, and RAND Corporation. == Artificial intelligence == Quinlan is a specialist in artificial intelligence, particularly in the aspect involving machine learning and its application to data mining. He is a Founding Fellow of the Association for the Advancement of Artificial Intelligence. === ID3 === Ross Quinlan invented the Iterative Dichotomiser 3 (ID3) algorithm which is used to generate decision trees. ID3 follows the principle of Occam's razor in attempting to create the smallest decision tree possible. === C4.5 === He then expanded upon the principles used in ID3 to create C4.5. C4.5 improved: discrete and continuous attributes, missing attribute values, attributes with differing costs, pruning trees (replacing irrelevant branches with leaf nodes). === C5.0 === C5.0, which Quinlan is commercially selling (single-threaded version is distributed under the terms of the GNU General Public License), is an improvement on C4.5. The advantages are speed (several orders of magnitude faster), memory efficiency, smaller decision trees, boosting (more accuracy), ability to weight different attributes, and winnowing (reducing noise). == Selected works == === Books === 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers. ISBN 1-55860-238-0. === Articles === Quinlan, J. R. (1982) Semi-autonomous acquisition of pattern-based knowledge, In Machine intelligence 10 (eds J. E. Hayes, D. Michie, and Y.-H. Pao). Ellis Norwood,Chichester. Quinlan, J.R. (1985). Decision trees and multi-valued attributes, In J.E. Hayes & D. Michie (Eds.), Machine intelligence 11. Oxford University Press. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1):81-106 2008. (with Qiang Yang, Philip S. Yu, Zhou Zhihua, and David Hand et al). Top 10 algorithms in data mining. Knowledge and Information Systems 14.1: 1-37 Quinlan, J. R. (1990). Learning logical definitions from relations. Machine Learning, 5:239-266.
Best AI Headshot Generators in 2026
In search of the best AI headshot generator? An AI headshot generator is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI headshot generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
How to Choose an AI Background Remover
Shopping for the best AI background remover? An AI background remover is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI background remover slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
Perusall
Perusall is a social web annotation tool intended for use by students at schools and universities. It allows users to annotate the margins of a text in a virtual group setting that is similar to social media—with upvoting, emojis, chat functionality, and notification. It also includes automatic AI grading. == History == Perusall began as a research project at Harvard University. It later became an educational product for students and teachers. As of 2024, Perusall states more than 5 million students have used the tool at over 5,000 educational institutions in 112 countries." == Functionality == Perusall integrates with learning management systems such as Moodle, Canvas and Blackboard to aid with collaborative annotation. The tool supports annotation of a range of media including text, images, equations, videos, PDFs and snapshots of webpages.
Michael Collins (computational linguist)
Michael J. Collins (born 4 March 1970) is a researcher in the field of computational linguistics. He is the Vikram S. Pandit Professor of Computer Science at Columbia University. His research interests are in natural language processing as well as machine learning and he has made important contributions in statistical parsing and in statistical machine learning. In his studies Collins covers a wide range of topics such as parse re-ranking, tree kernels, semi-supervised learning, machine translation and exponentiated gradient algorithms with a general focus on discriminative models and structured prediction. One notable contribution is a state-of-the-art parser for the Penn Wall Street Journal corpus. As of 11 November 2015, his works have been cited 16,020 times, and he has an h-index of 47. Collins worked as a researcher at AT&T Labs between January 1999 and November 2002, and later held the positions of assistant and associate professor at M.I.T. Since January 2011, he has been a professor at Columbia University. In 2011, he was named a fellow of the Association for Computational Linguistics.