Best AI tools for< Investigate Data Breaches >

CrowdStrike is a cloud-based cybersecurity platform that provides endpoint protection, threat intelligence, and incident response services. It uses artificial intelligence (AI) to detect and prevent cyberattacks. CrowdStrike's platform is designed to be scalable and easy to use, and it can be deployed on-premises or in the cloud. CrowdStrike has a global customer base of over 23,000 organizations, including many Fortune 500 companies.

CrowdStrike is a leading cybersecurity platform that uses artificial intelligence (AI) to protect businesses from cyber threats. The platform provides a unified approach to security, combining endpoint security, identity protection, cloud security, and threat intelligence into a single solution. CrowdStrike's AI-powered technology enables it to detect and respond to threats in real-time, providing businesses with the protection they need to stay secure in the face of evolving threats.

Nametag is an identity verification solution designed specifically for IT helpdesks. It helps businesses prevent social engineering attacks, account takeovers, and data breaches by verifying the identity of users at critical moments, such as password resets, MFA resets, and high-risk transactions. Nametag's unique approach to identity verification combines mobile cryptography, device telemetry, and proprietary AI models to provide unmatched security and better user experiences.

Quantexa News API is an AI-powered news data application that provides efficient, effective, and accurate access to global news at scale. It offers real-time access to enriched, tagged, and structured news feeds, enhancing risk monitoring processes and models. The application aggregates news content from over 90,000 sources and 1.3 million NLP-enriched news articles daily, with advanced AI-powered search capabilities. Users can investigate data through visualizations, automate tasks with entity and sentiment analysis, and easily share news data with relevant stakeholders.

SecureLabs is an AI-powered platform that offers comprehensive security, privacy, and compliance management solutions for businesses. The platform integrates cutting-edge AI technology to provide continuous monitoring, incident response, risk mitigation, and compliance services. SecureLabs helps organizations stay current and compliant with major regulations such as HIPAA, GDPR, CCPA, and more. By leveraging AI agents, SecureLabs offers autonomous aids that tirelessly safeguard accounts, data, and compliance down to the account level. The platform aims to help businesses combat threats in an era of talent shortages while keeping costs down.

Hatchet is an AI companion designed to assist on-call engineers in incident response by providing intelligent insights and suggestions based on logs, communications channels, and code analysis. It helps save time and money by automating the triaging and investigation process during critical incidents. The tool is built by engineers with a focus on data security, offering self-hosted deployments, permissions, audit trails, SSO, and version control. Hatchet aims to streamline incident resolution for tier-1 services, enabling faster response and potential problem resolution.

Megagon Labs is an AI research lab focusing on Knowledge Representation & Reasoning, Natural Language Processing, Human-Centered AI, and Data AI Symbiosis. They empower users with better information to make informed decisions through cutting-edge research and technologies. The lab's work includes investigating symbiotic systems, data management for AI, human-centered AI tools, and enhancing NLP capabilities for various domains.

iSEM.ai is an end-to-end AI-powered AML and Fraud Detection solution that empowers users to identify risks, investigate anomalies, and streamline reporting. The platform combines human intelligence with machine technology to adapt, reduce risks, and enhance efficiency in combating financial crimes. iSEM.ai offers tailored solutions to manage client data, onboard monitoring, client profile management, watchlist monitoring, transaction monitoring, transaction screening, and fraud monitoring. The application is designed to help businesses comply with regulations, detect suspicious activities, and ensure seamless protection at every step.

GitHub Next is a research and development team at GitHub that explores the future of software development. The team prototypes tools and technologies that will change the way we build software, and identifies new approaches to building healthy, productive software engineering teams.

Darktrace is a cybersecurity platform that leverages AI technology to provide proactive protection against cyber threats. It offers cloud-native AI security solutions for networks, emails, cloud environments, identity protection, and endpoint security. Darktrace's AI Analyst investigates alerts at the speed and scale of AI, mimicking human analyst behavior. The platform also includes services such as 24/7 expert support and incident management. Darktrace's AI is built on a unique approach where it learns from the organization's data to detect and respond to threats effectively. The platform caters to organizations of all sizes and industries, offering real-time detection and autonomous response to known and novel threats.

Overwatch Data is a comprehensive intelligence platform that offers real-time, global understanding for cyber, fraud, security, supply chain, and market intelligence needs. The platform provides concise, actionable insights tailored to the user's requirements, cutting through noise to deliver crucial information efficiently. With customizable monitoring options and intuitive data visualizations, Overwatch Data empowers users to stay informed and make informed decisions in the ever-evolving landscape of intelligence gathering.

Wondershare Recoverit is a comprehensive data recovery software that can restore lost files from various devices and storage media. It offers advanced features such as enhanced photo and video recovery, hard drive and location recovery, system crashed computer recovery, NAS data recovery, and Linux data recovery. Recoverit supports over 1000 file formats and 2000 storage devices, with a high recovery rate of 98%. It is trusted by over 5 million users across 160 countries and has been awarded 35 advanced patents for its innovative data recovery methods.

Relativity is an AI-powered eDiscovery and legal search software solution that helps customers organize data, discover truth, and act on it. It offers a range of features such as proactive security, user experience enhancements, open platform for customization, legendary support, and AI-powered review tools. Relativity is trusted by thousands of organizations to handle sensitive data and streamline data discovery processes.

Aura is an all-in-one digital safety platform that uses artificial intelligence (AI) to protect your family online. It offers a wide range of features, including financial fraud protection, identity theft protection, VPN & online privacy, antivirus, password manager & smart vault, parental controls & safe gaming, and spam call protection. Aura is easy to use and affordable, and it comes with a 60-day money-back guarantee.

Brighterion AI, a Mastercard company, offers advanced AI solutions for financial institutions, merchants, and healthcare providers. With over 20 years of experience, Brighterion has revolutionized AI by providing market-ready models that enhance customer experience, reduce financial fraud, and mitigate risks. Their solutions are enriched with Mastercard's global network intelligence, ensuring scalability and powerful personalization. Brighterion's AI applications cater to acquirers, PSPs, issuers, and healthcare providers, offering custom AI solutions for transaction fraud monitoring, merchant monitoring, AML & compliance, and healthcare fraud detection. The company has received several prestigious awards for its excellence in AI and financial security.

Quantifind is an AI-powered financial crimes automation platform that specializes in Anti-Money Laundering (AML) and Know Your Customer (KYC) solutions. It offers end-to-end automation impact, best-in-class accuracy, and powerful APIs and applications for risk screening, investigations, and compliance in the financial services and public sector industries. Quantifind's Graphyte platform leverages AI and external data to streamline AML-KYC processes, providing comprehensive data coverage, dynamic risk typologies, and seamless integrations with case management systems.

EchoMark is a cloud-based data leak prevention solution that uses invisible forensic watermarks to protect sensitive information from unauthorized access and exfiltration. It allows organizations to securely share and collaborate on documents and emails without compromising privacy and security. EchoMark's advanced investigation tools can trace the source of a leaked document or email, even if it has been shared via printout or photo.

Amped Software develops solutions for the analysis and enhancement of images and videos for forensic, security, and investigative applications. Their tools are used by top forensic labs, law enforcement, military, security, and government agencies worldwide.

Trust Stamp is an AI-powered digital identity solution that focuses on mitigating fraud through biometrics, privacy, and cybersecurity. The platform offers secure authentication and multi-factor authentication using biometric data, along with features like KYC/AML compliance, tokenization, and age estimation. Trust Stamp helps financial institutions, healthcare providers, dating platforms, and other industries prevent identity theft and fraud by providing innovative solutions for account recovery and user security.

NICE Actimize is an AI-driven platform that offers solutions for combatting financial crime, including Anti-Money Laundering (AML), Enterprise Fraud Management, Financial Markets Compliance, Investigation and Case Management, and Data Intelligence. The platform utilizes AI and machine learning to optimize efficacy, accuracy, and regulatory compliance coverage in the fight against financial crime.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

TinyTroupe is an experimental Python library that leverages Large Language Models (LLMs) to simulate artificial agents called TinyPersons with specific personalities, interests, and goals in simulated environments. The focus is on understanding human behavior through convincing interactions and customizable personas for various applications like advertisement evaluation, software testing, data generation, project management, and brainstorming. The tool aims to enhance human imagination and provide insights for better decision-making in business and productivity scenarios.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.

The 'duckdb-airport-extension' is a tool that enables the use of Arrow Flight with DuckDB. It provides functions to list available Arrow Flights at a specific endpoint and to retrieve the contents of an Arrow Flight. The extension also supports creating secrets for authentication purposes. It includes features for serializing filters and optimizing projections to enhance data transmission efficiency. The tool is built on top of gRPC and the Arrow IPC format, offering high-performance data services for data processing and retrieval.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.

LLM-Geo is an AI-powered geographic information system (GIS) that leverages Large Language Models (LLMs) for automatic spatial data collection, analysis, and visualization. By adopting LLM as the reasoning core, it addresses spatial problems with self-generating, self-organizing, self-verifying, self-executing, and self-growing capabilities. The tool aims to make spatial analysis easier, faster, and more accessible by reducing manual operation time and delivering accurate results through case studies. It uses GPT-4 API in a Python environment and advocates for further research and development in autonomous GIS.

STILL is an open-source project exploring slow-thinking reasoning systems, focusing on o1-like reasoning systems. The project has released technical reports on enhancing LLM reasoning with reward-guided tree search algorithms and implementing slow-thinking reasoning systems using an imitate, explore, and self-improve framework. The project aims to replicate the capabilities of industry-level reasoning systems by fine-tuning reasoning models with long-form thought data and iteratively refining training datasets.

AgentGym is a framework designed to help the AI community evaluate and develop generally-capable Large Language Model-based agents. It features diverse interactive environments and tasks with real-time feedback and concurrency. The platform supports 14 environments across various domains like web navigating, text games, house-holding tasks, digital games, and more. AgentGym includes a trajectory set (AgentTraj) and a benchmark suite (AgentEval) to facilitate agent exploration and evaluation. The framework allows for agent self-evolution beyond existing data, showcasing comparable results to state-of-the-art models.

Robusta is a tool designed to enhance Prometheus notifications for Kubernetes environments. It offers features such as smart grouping to reduce notification spam, AI investigation for alert analysis, alert enrichment with additional data like pod logs, self-healing capabilities for defining auto-remediation rules, advanced routing options, problem detection without PromQL, change-tracking for Kubernetes resources, auto-resolve functionality, and integration with various external systems like Slack, Teams, and Jira. Users can utilize Robusta with or without Prometheus, and it can be installed alongside existing Prometheus setups or as part of an all-in-one Kubernetes observability stack.

This repository contains convenience scripts to finetune LLaMa3-8B (or any other foundation model) for chat towards any language (that isn't English). The rationale behind this is that LLaMa3 is trained on primarily English data and while it works to some extent for other languages, its performance is poor compared to English.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

HolmesGPT is an open-source DevOps assistant powered by OpenAI or any tool-calling LLM of your choice. It helps in troubleshooting Kubernetes, incident response, ticket management, automated investigation, and runbook automation in plain English. The tool connects to existing observability data, is compliance-friendly, provides transparent results, supports extensible data sources, runbook automation, and integrates with existing workflows. Users can install HolmesGPT using Brew, prebuilt Docker container, Python Poetry, or Docker. The tool requires an API key for functioning and supports OpenAI, Azure AI, and self-hosted LLMs.

The AI-Exchange Protocol (AIXP) is a communication standard designed to facilitate information and result exchange between artificial intelligence agents. It aims to enhance interoperability and collaboration among various AI systems by establishing a common framework for communication. AIXP includes components for communication, loop prevention, and task finalization, ensuring secure and efficient collaboration while avoiding infinite communication loops. The protocol defines access points, data formats, authentication, authorization, versioning, loop detection, status codes, error messages, and task completion verification. AIXP enables AI agents to collaborate seamlessly and complete tasks effectively, contributing to the overall efficiency and reliability of AI systems.

Autonomous agents are designed to achieve specific objectives through self-guided instructions. With the emergence and growth of large language models (LLMs), there is a growing trend in utilizing LLMs as fundamental controllers for these autonomous agents. This repository conducts a comprehensive survey study on the construction, application, and evaluation of LLM-based autonomous agents. It explores essential components of AI agents, application domains in natural sciences, social sciences, and engineering, and evaluation strategies. The survey aims to be a resource for researchers and practitioners in this rapidly evolving field.

LLaMa2lang is a repository containing convenience scripts to finetune LLaMa3-8B (or any other foundation model) for chat towards any language that isn't English. The repository aims to improve the performance of LLaMa3 for non-English languages by combining fine-tuning with RAG. Users can translate datasets, extract threads, turn threads into prompts, and finetune models using QLoRA and PEFT. Additionally, the repository supports translation models like OPUS, M2M, MADLAD, and base datasets like OASST1 and OASST2. The process involves loading datasets, translating them, combining checkpoints, and running inference using the newly trained model. The repository also provides benchmarking scripts to choose the right translation model for a target language.

MathVerse is an all-around visual math benchmark designed to evaluate the capabilities of Multi-modal Large Language Models (MLLMs) in visual math problem-solving. It collects high-quality math problems with diagrams to assess how well MLLMs can understand visual diagrams for mathematical reasoning. The benchmark includes 2,612 problems transformed into six versions each, contributing to 15K test samples. It also introduces a Chain-of-Thought (CoT) Evaluation strategy for fine-grained assessment of output answers.

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