Predict and Prevent Cyber Threats with Data
AIS is a deep learning model trained from vast amounts of data to make predictions about attacks or disruptions across a variety of ecosystems. It uses artificial intelligence algorithms and Artificial Entities (AEs) to transform raw data into information and knowledge to warn about future bad actions, learned threats, and vulnerabilities. AIS provides indications and warnings to make informed decisions based on the trained activity.
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It uses a Bayesian Recurrent Neural Network (BN) as the base of the deep learning model where connections between cells form a directed graph along a temporal sequence creating the neuron chain and uses the BN to determine explicit delineations of uncertainties in the data, therefore enhancing the likely successful predictions of the outcomes.
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AIS can predict and forecast issues across any cyber ecosystem. As an example, AIS can create models to monitor supply chains (SC). The system can model interconnected networks and related SC technologies, and forecasts potential vulnerabilities and attacks against the system.
Provides Insight to Make Informed Decisions
AIS predicts outcomes based on your input activity. It provides insight to make informed decisions to secure your global supply chain using AI to learn and predict cyber-attacks across the supply chain ecosystem and lets decision makers understand how their choices would impact those risks.
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AIS has a gamification mode, where it can model millions of years of simulated attacks on any ecosystem. AIS uses this simulated data to train an AI model to make predictions on most likely attacks and the success of those attacks. This not only provides decision makers ample time to determine the best course of action, to include what counter measures that are needed, but AIS will provide insight on their choice’s likelihood of success in countering the attack before any monetary or time investments are made.
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Risks arise from internal and external threats, with either malicious or accidental intent. Examples of vulnerabilities may include risk of misappropriation, breach of confidentiality, adulteration (malware), or destruction. AIS has been trained to predict risk and attacks (cyber, terrorist, criminal, military, and any action that resembles or has signatures of the “Attack Cycle”) across the multiple ecosystems.
Provides Inherent Information Supremacy
The architecture provides inherent information supremacy by defining and binding all the information derived from or about your data into a structure that can be transformed into knowledge to allow for informed choices and supremacy over your cyber ecosystem.
What Differentiates AIS from Similar Services
AIS is a proactive artificial intelligence that provides deep insight into your ecosystem risks and is proactive in forecasting attacks against your ecosystem. AIS doesn’t rely on fancy monitoring or hardware solutions, but “learns” from your existing data. It not only integrates into your current solutions but can integrate into future solutions. It is not dependent on any proprietary technologies.
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As previously stated, AIS models millions of simulated attacks on your ecosystem based on its trained AI model to make predictions on most likely attacks and the likelihood of success of those attacks. This not only provides decision makers ample time to determine the best course of action, to include what counter measures are needed, but AIS will provide insight on their choice’s likelihood of success in countering the attack before any monetary or time investments are made. AIS’s social media proactive cells not only monitor bad actors but learn from them.
Indications & Warnings Add-On
AIS coupled with Knowledge Graph technology lets us identify clusters of interactions between individuals to identify means of exploitation, collect data, and send to AIS to forecast global events. This Indications and Warnings Add-On continuously monitors open source and internal data for new fraudulent activities as well as bad actors, and stops them at the time of the event by providing indications and warnings of potential global military, criminal, or geopolitical activities.
The deep learning model collects and analyzes data from both internal and external data sources, including hundreds of public social media and websites, and identifies and scores the different clusters of bad actors planning unlawful activities. It then targets those bad actor networks and assesses vulnerabilities, and exploits vectors against those networks. Scoring is based on previous prediction accuracy.
Advantages
Flexible
Works with block chain technologies and can be incorporated into smart contracts.
Multi-directional Inferences
The AIS model once trained can be used to either predict (defensive/inference) or create synthetic "input" data based on a simulated outcome.
Integratable
Can be integrated into other AI models to include LLMs
Continuous Data Collection
Uses pre-trained date cells to organize data into specific data cells
Uses Your Data
Internally and proprietary data can be used to securely train or reenforce training of existing models as "bolt-on" data
Knowledge Sharing
Connects securely to other instances of AIS running to share knowledge.
Secure
Innately secure by function, not design – it doesn’t store confidential data, it learns from your confidential data.
Provides Choice
Predicts the risks based on choices of policy, process, or technology investments on the ecosystem.
Encrypted Data Sharing
Encrypted to provide for secure sharing of threat, vulnerability, ecosystem, and classification information without compromising any underlying data on the proprietary ecosystem.
Storage Options
AIS can be run on premises or in the cloud – no new hardware investments.