A MIT-based AI apps startup is advancing to new heights in the cybersecurity field by developing innovative technologies to prevent supply chain attacks through artificial intelligence. Cybercriminals are becoming increasingly interested in targeting supply chains. To prevent damage to supply chains, this innovative startup aims to offer robust protection.
Providing seamless cross-border flows of goods and services is one of the most significant elements of supply chains as they support a global economy. In recent years, they have grown increasingly vulnerable to cyberattacks, with malicious actors targeting vulnerabilities in digital infrastructure to disrupt operations and compromise sensitive data. This is in a bid to disrupt these operations and compromise the security of sensitive data. As the start-up recognizes the dire need for supply chain security in an increasingly complex environment, it is now paving the way for the next era of supply chain security built on robust defense mechanisms.
Businesses and organizations around the world are exposed to significant threats due to supply chain attacks. A malicious actor who infiltrates a vulnerable point in a supply chain can compromise critical systems, steal sensitive information, or introduce malicious software into the system. This is the result of compromising vulnerable points. To create a resilient shield against cyber threats, the MIT startup has leveraged artificial intelligence to implement a proactive defense mechanism that recognizes the need for proactive measures.
There are reports that manufacturers are facing supply chain attacks that demand ransomware amounts that are twice or three times the ransomware demands made in other industries, according to the manufacturers.
In fact, it can cost millions of dollars to stop a production line. The majority of small to mid-sized single-location manufacturers who are targeted make a ransom payment, then scramble to locate cybersecurity advice to be able to prevent another security breach in the future. As a result, it is not uncommon for them to become victims again from time to time.
The threat of ransomware remains the way of choice for cybercriminal groups looking to gain financial benefit from attacks on supply chains. One of the most infamous attacks targeted Aebi Schmidt, ASCO, COSCO, Eurofins Scientific, Norsk Hydro, and Titan Manufacturing and Distributing, among others. Several other major victims have requested anonymity for security reasons. A disaster of a similar nature occurred in the Danish shipping conglomerate A.P. Meller-Maersk, which is part of the Danish shipping conglomerate A.P. Meller Group. A number of other cargo terminals in Los Angeles were also affected by this attack, which shut down the city's largest terminal.
It is not only the focus on advanced cybersecurity techniques that sets this MIT startup apart from other startups.
This technology uses AI-powered solutions to augment traditional rules-based systems rather than relying solely on rule-based solutions to respond quickly and adaptively to evolving threats.
Due to the system's adaptive nature, the system is able to stay one step ahead of cybercriminals, learning from new threats continually and constantly improving its defenses to keep up with them.
Further, the startup has a commitment to collaborating with industry partners, organizations, and cybersecurity experts in order to further enhance its capabilities. It is their intention to strengthen the resilience of supply chains on a global scale by fostering a globally accessible ecosystem of sharing information and collective defense. By implementing a collaborative approach, threat intelligence can be disseminated rapidly and countermeasures can be developed more quickly and effectively.
Mid-tier and small manufacturers in particular find it difficult to manage supplier risk effectively because they are already shorthanded when it comes to their IT and cybersecurity departments. Standards and technologies that are scalable are what they need. A new standard that was developed by the National Institute of Standards and Technology (NIST) is intended to manage risk associated with cybersecurity supply chains for systems and organizations. It aims to provide a guide that is intended to assist supply chain managers in identifying, assessing, and addressing cyber threats throughout their supply chains.
This standard is the result of a follow-up capstone report published one year later, Executive Order on America's Supply Chains: A Year of Action and Progress. This report was the result of President Biden's initial executive order on America's Supply Chains, issued on February 24, 2021. NIST provides a framework for hardening supply chain cybersecurity.
As a result of the cofounders’ research at MIT, an AI app platform has been created by Ikigai Labs, which was designed to use large graphical models (LGMs) and expertise-in-the-loop (EiTL) for AI Applications based on their research. This feature allows the system to gather inputs from experts in real-time and to continuously learn to maximize AI-driven insights, expertise and intuition. There are currently several AI Apps that are being used by Ikigai to optimize supply chains (labor planning, sales, operations), retail (demand forecasting, new product launches), insurance (auditing rate-making), financial services (compliance know-your-customer), banking (reconciliation of transactions between customer entities) and manufacturing (predictive maintenance and quality assurance); and there is much more on the list of possible uses as well.
By using expert-in-the-loop (EiTL) workflows that continuously improve the accuracy of the LGM models which LKigai uses, this approach will be able to solve cybersecurity challenges related to supply chains. In order to improve the effectiveness and results of MDR, it would be beneficial to combine LGM models with EiTL techniques.
There is a constant challenge that every enterprise faces when trying to make sense of siloed and incomplete data that is spread throughout the organization. In fact, data gaps are among the most constraining aspects of most organizations' most difficult, complex problems. These specialized strategic areas require a high level of strategic planning, which is not something that can be achieved by existing methods of mining data.
By working with sparse, limited datasets, Ikigai's AI Apps platform helps solve these challenges by delivering needed insight and intelligence through the use of LGMs that are capable of doing so.
DeepPlan includes the ability to prepare data using deep learning, DeepMatch to optimize the preparation of data through AI and DeepCast to model predictive models with sparse data using machine learning and one-click MLOps. It is thanks to Ikigai's advanced technology that advanced features such as EiTL are possible in its products.
Incorporating human expertise into EiTL with LGM models improves model accuracy as the model is more accurate. When it comes to managed detection and response (MDR) scenarios, EiTL would work with human expertise to detect upcoming threats and fraud patterns by combining it with learning models. Using EiTL's real-time inputs into the AI system, MDR teams is able to detect threats more quickly and respond more effectively to those threats.
In combining the LGM and EiTL technologies provided by Ikigai, the Ikigai AI platform can identify fraud, intrusions, and breaches that can be stopped and prevented through its use of artificial intelligence. To ensure that only transactions involving known identities will be allowed, this procedure is followed. Additionally, the ways in which Ikigai creates applications are versatile enough to enforce least privileged access and to audit every session which occurs between an identity and its resources as well as to enforce least privilege access. Zero-trust security relies on both of these components as its key components.
The AI infrastructure developed by Ikigai is designed so that people who lack technical expertise can easily use it to develop apps and predictive models that can be scaled across an organization on an immediate basis.
Key elements of the platform include DeepMatch, DeepCast and DeepPlan. DeepMatch matches rows based on dataset columns. Using DeepCast, you will be able to make predictions with little data when using spatial and temporal data structures. It is made possible for decision-makers to create scenarios using historical data by DeepPlan using historical data.
There is an increasing need for robust cybersecurity measures to be implemented in the context of expanding supply chains that are becoming increasingly digitized. A new MIT startup is demonstrating how artificial intelligence technology can be incorporated into supply chains to make them more resilient to cyberattacks.
It is believed that global supply networks will be more secure and resilient in the future due to them harnessing the power of advanced algorithms and collaboration in order to use the latest technological advancements
MIT startup MITid is playing a leading role in harnessing the power of artificial intelligence to secure supply chains in an era where supply chains are increasingly vulnerable to cyber attack.