Best AI tools for< Monitor Data Exposures >

Knostic AI is an AI application that focuses on Copilot Readiness for Enterprise AI Security. It helps organizations locate and remediate data leaks from AI searches, ensuring data security and compliance. Knostic offers solutions to prevent data leakage, map knowledge boundaries, recommend permission adjustments, and provide independent verification of security posture readiness for AI adoption.

Traceable is an AI-driven application designed to enhance API security for Cloud-Native Apps. It collects API traffic across the application landscape and utilizes advanced context-based behavioral analytics AI engine to provide insights on APIs, data exposure, threat analytics, and forensics. The platform offers features for API cataloging, activity monitoring, endpoint details, ownership, vulnerabilities, protection against security events, testing, analytics, and more. Traceable also allows for role-based access control, policy configuration, data classification, and integration with third-party solutions for data collection and security. It is a comprehensive tool for API security and threat detection in modern cloud environments.

PressPulse AI is a next-generation platform that serves as an alternative to HARO (Help A Reporter Out) for experts and media resources. It connects experts and journalists by providing personalized opportunities and AI-assisted pitch drafting. With features like tailored opportunities, domain authority data access, and first-mover notifications, PressPulse AI aims to streamline the media engagement process for professionals seeking media coverage. The platform offers different subscription tiers with varying features to cater to different PR needs.

Browse AI is an AI-powered data extraction and monitoring platform that allows users to scrape and monitor data from any website without the need for coding. It offers a full suite of features for stress-free data extraction, including turning websites into APIs, monitoring for changes, and creating prebuilt robots for various use cases. With over 7,000 integrations, Browse AI ensures reliable and scalable data extraction with no coding required. The platform is trusted by over 558,000 users worldwide and is designed to simplify the process of turning any website into a reliable data pipeline.

Lightup is a cloud data quality monitoring tool with AI-powered anomaly detection, incident alerts, and data remediation capabilities for modern enterprise data stacks. It specializes in helping large organizations implement successful and sustainable data quality programs quickly and easily. Lightup's pushdown architecture allows for monitoring data content at massive scale without moving or copying data, providing extreme scalability and optimal automation. The tool empowers business users with democratized data quality checks and enables automatic fixing of bad data at enterprise scale.

Evidently AI is an open-source machine learning (ML) monitoring and observability platform that helps data scientists and ML engineers evaluate, test, and monitor ML models from validation to production. It provides a centralized hub for ML in production, including data quality monitoring, data drift monitoring, ML model performance monitoring, and NLP and LLM monitoring. Evidently AI's features include customizable reports, structured checks for data and models, and a Python library for ML monitoring. It is designed to be easy to use, with a simple setup process and a user-friendly interface. Evidently AI is used by over 2,500 data scientists and ML engineers worldwide, and it has been featured in publications such as Forbes, VentureBeat, and TechCrunch.

Qubinets is a cloud data environment solutions platform that provides building blocks for building big data, AI, web, and mobile environments. It is an open-source, no lock-in, secured, and private platform that can be used on any cloud, including AWS, Digital Ocean, Google Cloud, and Microsoft Azure. Qubinets makes it easy to plan, build, and run data environments, and it streamlines and saves time and money by reducing the grunt work in setup and provisioning.

illumex is a generative semantic fabric platform designed to streamline the process of data and analytics interpretation and rationalization for complex enterprises. It offers augmented analytics creation, suggestive data and analytics utilization monitoring, and automated knowledge documentation to enhance agentic performance for analytics. The platform aims to solve the challenges of traditional tedious data analysis, incongruent data and metrics, and tribal knowledge of data teams.

Fastn is a no-code, AI-powered orchestration platform for developers to integrate and orchestrate multiple data sources in a single, unified API. It allows users to connect any data flow and create hundreds of app integrations efficiently. Fastn simplifies API integration, ensures API security, and handles data from multiple sources with features like real-time data orchestration, instant API composition, and infrastructure management on autopilot.

Marketing Dive is an AI-powered digital marketing news platform that provides industry professionals with the latest updates, trends, and insights in the marketing world. The platform covers a wide range of topics including social media, brand strategy, advertising, marketing technology, data/analytics, and content marketing. Marketing Dive offers daily newsletters, weekly updates, and in-depth articles to keep marketers informed and engaged with the rapidly evolving landscape of digital marketing.

MagicLoop is a voice survey tool designed to enhance customer feedback by replacing written feedback with spoken responses. It allows users to gather higher-quality responses through voice surveys, capturing emotions, tones, and nuances for a deeper understanding of participants' feelings and intentions. The tool aims to improve participant engagement and provide detailed insights by encouraging genuine responses. MagicLoop offers a modern approach to surveys, addressing the limitations of traditional methods and providing tailored solutions for various use cases such as user research, satisfaction surveys, NPS, feedback collection, market research, and data monitoring. With features like AI analysis, speech-to-text transcription, and custom branding, MagicLoop streamlines the process of generating insights from voice recordings.

OpenLIT is an AI application designed as an Observability tool for GenAI and LLM applications. It empowers model understanding and data visualization through an interactive Learning Interpretability Tool. With OpenTelemetry-native support, it seamlessly integrates into projects, offering features like fine-tuning performance, real-time data streaming, low latency processing, and visualizing data insights. The tool simplifies monitoring with easy installation and light/dark mode options, connecting to popular observability platforms for data export. Committed to OpenTelemetry community standards, OpenLIT provides valuable insights to enhance application performance and reliability.

Browse AI is a web data extraction and monitoring platform that makes it easy, affordable, and reliable for anyone to collect data from the web at scale. It was founded in 2020 with the mission of making the web more accessible and useful for everyone.

Nume is an AI CFO application designed for startup founders to manage their finances efficiently. It offers features such as monitoring financial data, generating reports instantly, providing insights, drafting detailed updates, and answering financial questions. Nume aims to alleviate the financial worries of founders by automating financial tasks and providing valuable insights to help businesses grow.

MedoSync is an AI-driven health platform that empowers users to monitor and analyze their vital and medical data, leveraging AI to provide personalized insights and recommendations for a healthier life. Users can upload lab results, digitize medical documents, use an AI symptom checker, create accounts for family members, and integrate with their healthcare system. The platform offers easy data export, accuracy in health insights, and personalized health recommendations, with a high user satisfaction rate.

Medical Brain is an AI-powered clinical assistant designed for both patients and providers. It engages with users to identify health risks and care gaps early, providing actionable insights and guidance to improve outcomes and intercept high-cost ER visits. The platform monitors patients 24/7, aggregates and understands all patient data, and generates real-time actions based on AI clinical decision support and automation. Medical Brain incorporates evidence-based best practices in various clinical modules and continuously learns from user experiences to enhance efficiency and intelligence.

OLY.AI is an AI-powered bookkeeping solution designed for QuickBooks Online users. It provides instant answers to metric business questions, real-time data analysis, and secure integration with QuickBooks Online. OLY.AI offers custom GPTs for company-specific insights, real-time charts for monitoring KPI data, and 24/7 availability for answering queries. The platform prioritizes data privacy and security by using advanced encryption technology to protect user data. OLY.AI aims to empower metric-driven company culture and facilitate data-driven decision-making.

CookieChimp is a modern consent management platform designed to help websites effortlessly collect user consent for 3rd party services while ensuring compliance with various privacy standards such as GDPR, CCPA, TCF 2.2, and Google Consent Mode. The platform offers features like dashboard monitoring, automated cookie scanning, customizable consent banners, integrations with CRM and marketing tools, global compliance solutions, robust consent logging, and AI-powered efficiency. CookieChimp is trusted by modern companies for its user-friendly interface, valuable insights, and quick setup process.

AIOZ Network is an AI-powered platform that focuses on Web3, AI, storage, and streaming services. It offers decentralized AI computation, fast and reliable storage solutions, and seamless video streaming for dApps within the network. AIOZ aims to empower a fast, secure, and decentralized future by providing a one-click integration of dApps on the AIOZ blockchain, supporting popular smart contract languages, and utilizing spare computing resources from a global community of nodes.

Portaly is a comprehensive tool designed for creators to manage content, grow their fan base, and monetize traffic. With over 100,000 creators onboard, Portaly offers features like setting up micro-websites, integrating social media content, managing fan lists, handling business transactions, selling digital products, and monitoring traffic data. It serves as a one-stop solution for content monetization by integrating payment systems, creating personalized pages, and driving traffic from various social platforms. Portaly is the largest Chinese portal tool globally, connecting Instagram, YouTube, Podcasts, and TikTok. It leverages AI to assist in creating personalized websites to achieve marketing and sales goals.

ztachip is a RISCV accelerator designed for vision and AI edge applications, offering up to 20-50x acceleration compared to non-accelerated RISCV implementations. It features an innovative tensor processor hardware to accelerate various vision tasks and TensorFlow AI models. ztachip introduces a new tensor programming paradigm for massive processing/data parallelism. The repository includes technical documentation, code structure, build procedures, and reference design examples for running vision/AI applications on FPGA devices. Users can build ztachip as a standalone executable or a micropython port, and run various AI/vision applications like image classification, object detection, edge detection, motion detection, and multi-tasking on supported hardware.

Gateway is a tool that streamlines requests to 100+ open & closed source models with a unified API. It is production-ready with support for caching, fallbacks, retries, timeouts, load balancing, and can be edge-deployed for minimum latency. It is blazing fast with a tiny footprint, supports load balancing across multiple models, providers, and keys, ensures app resilience with fallbacks, offers automatic retries with exponential fallbacks, allows configurable request timeouts, supports multimodal routing, and can be extended with plug-in middleware. It is battle-tested over 300B tokens and enterprise-ready for enhanced security, scale, and custom deployments.

Azure OpenAI Service is a central capability with Azure API Management, providing guidance and tools for organizations to implement Azure OpenAI in a production environment with an emphasis on cost control, secure access, and usage monitoring. It includes infrastructure-as-code templates, CI/CD pipelines, secure access management, usage monitoring, load balancing, streaming requests, and end-to-end samples like ChatApp and Azure Dashboards.

MFLUX is a line-by-line port of the FLUX implementation in the Huggingface Diffusers library to Apple MLX. It aims to run powerful FLUX models from Black Forest Labs locally on Mac machines. The codebase is minimal and explicit, prioritizing readability over generality and performance. Models are implemented from scratch in MLX, with tokenizers from the Huggingface Transformers library. Dependencies include Numpy and Pillow for image post-processing. Installation can be done using `uv tool` or classic virtual environment setup. Command-line arguments allow for image generation with specified models, prompts, and optional parameters. Quantization options for speed and memory reduction are available. LoRA adapters can be loaded for fine-tuning image generation. Controlnet support provides more control over image generation with reference images. Current limitations include generating images one by one, lack of support for negative prompts, and some LoRA adapters not working.

LLM Zoomcamp is a free online course focusing on real-life applications of Large Language Models (LLMs). Over 10 weeks, participants will learn to build an AI bot capable of answering questions based on a knowledge base. The course covers topics such as LLMs, RAG, open-source LLMs, vector databases, orchestration, monitoring, and advanced RAG systems. Pre-requisites include comfort with programming, Python, and the command line, with no prior exposure to AI or ML required. The course features a pre-course workshop and is led by instructors Alexey Grigorev and Magdalena Kuhn, with support from sponsors and partners.

1Panel is an open-source, modern web-based control panel for Linux server management. It provides efficient management through a user-friendly web graphical interface, enabling users to effortlessly manage their Linux servers. Key features include host monitoring, file management, database administration, container management, rapid website deployment with WordPress integration, an application store for easy installation and updates, security and reliability through containerization and secure application deployment practices, integrated firewall management, log auditing capabilities, and one-click backup & restore functionality supporting various cloud storage solutions.

Cortex is a tool that simplifies and accelerates the process of creating applications utilizing modern AI models like chatGPT and GPT-4. It provides a structured interface (GraphQL or REST) to a prompt execution environment, enabling complex augmented prompting and abstracting away model connection complexities like input chunking, rate limiting, output formatting, caching, and error handling. Cortex offers a solution to challenges faced when using AI models, providing a simple package for interacting with NL AI models.

Awesome MCP Servers is a curated list of Model Context Protocol (MCP) servers that enable AI models to securely interact with local and remote resources through standardized server implementations. The list includes production-ready and experimental servers that extend AI capabilities through file access, database connections, API integrations, and other contextual services.

RLAMA is a powerful AI-driven question-answering tool that seamlessly integrates with local Ollama models. It enables users to create, manage, and interact with Retrieval-Augmented Generation (RAG) systems tailored to their documentation needs. RLAMA follows a clean architecture pattern with clear separation of concerns, focusing on lightweight and portable RAG capabilities with minimal dependencies. The tool processes documents, generates embeddings, stores RAG systems locally, and provides contextually-informed responses to user queries. Supported document formats include text, code, and various document types, with troubleshooting steps available for common issues like Ollama accessibility, text extraction problems, and relevance of answers.

Contoso Chat is a Python sample demonstrating how to build, evaluate, and deploy a retail copilot application with Azure AI Studio using Promptflow with Prompty assets. The sample implements a Retrieval Augmented Generation approach to answer customer queries based on the company's product catalog and customer purchase history. It utilizes Azure AI Search, Azure Cosmos DB, Azure OpenAI, text-embeddings-ada-002, and GPT models for vectorizing user queries, AI-assisted evaluation, and generating chat responses. By exploring this sample, users can learn to build a retail copilot application, define prompts using Prompty, design, run & evaluate a copilot using Promptflow, provision and deploy the solution to Azure using the Azure Developer CLI, and understand Responsible AI practices for evaluation and content safety.

Paddler is an open-source load balancer and reverse proxy designed specifically for optimizing servers running llama.cpp. It overcomes typical load balancing challenges by maintaining a stateful load balancer that is aware of each server's available slots, ensuring efficient request distribution. Paddler also supports dynamic addition or removal of servers, enabling integration with autoscaling tools.

The vscode-pddl extension provides comprehensive support for Planning Domain Description Language (PDDL) in Visual Studio Code. It enables users to model planning domains, validate them, industrialize planning solutions, and run planners. The extension offers features like syntax highlighting, auto-completion, plan visualization, plan validation, plan happenings evaluation, search debugging, and integration with Planning.Domains. Users can create PDDL files, run planners, visualize plans, and debug search algorithms efficiently within VS Code.

WilmerAI is a middleware system designed to process prompts before sending them to Large Language Models (LLMs). It categorizes prompts, routes them to appropriate workflows, and generates manageable prompts for local models. It acts as an intermediary between the user interface and LLM APIs, supporting multiple backend LLMs simultaneously. WilmerAI provides API endpoints compatible with OpenAI API, supports prompt templates, and offers flexible connections to various LLM APIs. The project is under heavy development and may contain bugs or incomplete code.

This Helm chart bootstraps an Airflow deployment on a Kubernetes cluster using the Helm package manager. The version of this chart does not correlate to any other component. Users should not expect feature parity between OSS airflow chart and the Astronomer airflow-chart for identical version numbers. To install this helm chart remotely (using helm 3) kubectl create namespace airflow helm repo add astronomer https://helm.astronomer.io helm install airflow --namespace airflow astronomer/airflow To install this repository from source sh kubectl create namespace airflow helm install --namespace airflow . Prerequisites: Kubernetes 1.12+ Helm 3.6+ PV provisioner support in the underlying infrastructure Installing the Chart: sh helm install --name my-release . The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation. Upgrading the Chart: First, look at the updating documentation to identify any backwards-incompatible changes. To upgrade the chart with the release name `my-release`: sh helm upgrade --name my-release . Uninstalling the Chart: To uninstall/delete the `my-release` deployment: sh helm delete my-release The command removes all the Kubernetes components associated with the chart and deletes the release. Updating DAGs: Bake DAGs in Docker image The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (`docker build -t my-company/airflow:8a0da78 .`), push it to an accessible registry (`docker push my-company/airflow:8a0da78`), then update the Airflow pods with that image: sh helm upgrade my-release . --set images.airflow.repository=my-company/airflow --set images.airflow.tag=8a0da78 Docker Images: The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow. Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor. Parameters: The complete list of parameters supported by the community chart can be found on the Parameteres Reference page, and can be set under the `airflow` key in this chart. The following tables lists the configurable parameters of the Astronomer chart and their default values. | Parameter | Description | Default | | :----------------------------- | :-------------------------------------------------------------------------------------------------------- | :---------------------------- | | `ingress.enabled` | Enable Kubernetes Ingress support | `false` | | `ingress.acme` | Add acme annotations to Ingress object | `false` | | `ingress.tlsSecretName` | Name of secret that contains a TLS secret | `~` | | `ingress.webserverAnnotations` | Annotations added to Webserver Ingress object | `{}` | | `ingress.flowerAnnotations` | Annotations added to Flower Ingress object | `{}` | | `ingress.baseDomain` | Base domain for VHOSTs | `~` | | `ingress.auth.enabled` | Enable auth with Astronomer Platform | `true` | | `extraObjects` | Extra K8s Objects to deploy (these are passed through `tpl`). More about Extra Objects. | `[]` | | `sccEnabled` | Enable security context constraints required for OpenShift | `false` | | `authSidecar.enabled` | Enable authSidecar | `false` | | `authSidecar.repository` | The image for the auth sidecar proxy | `nginxinc/nginx-unprivileged` | | `authSidecar.tag` | The image tag for the auth sidecar proxy | `stable` | | `authSidecar.pullPolicy` | The K8s pullPolicy for the the auth sidecar proxy image | `IfNotPresent` | | `authSidecar.port` | The port the auth sidecar exposes | `8084` | | `gitSyncRelay.enabled` | Enables git sync relay feature. | `False` | | `gitSyncRelay.repo.url` | Upstream URL to the git repo to clone. | `~` | | `gitSyncRelay.repo.branch` | Branch of the upstream git repo to checkout. | `main` | | `gitSyncRelay.repo.depth` | How many revisions to check out. Leave as default `1` except in dev where history is needed. | `1` | | `gitSyncRelay.repo.wait` | Seconds to wait before pulling from the upstream remote. | `60` | | `gitSyncRelay.repo.subPath` | Path to the dags directory within the git repository. | `~` | Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`. For example, sh helm install --name my-release --set executor=CeleryExecutor --set enablePodLaunching=false . Walkthrough using kind: Install kind, and create a cluster We recommend testing with Kubernetes 1.25+, example: sh kind create cluster --image kindest/node:v1.25.11 Confirm it's up: sh kubectl cluster-info --context kind-kind Add Astronomer's Helm repo sh helm repo add astronomer https://helm.astronomer.io helm repo update Create namespace + install the chart sh kubectl create namespace airflow helm install airflow -n airflow astronomer/airflow It may take a few minutes. Confirm the pods are up: sh kubectl get pods --all-namespaces helm list -n airflow Run `kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow` to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working. Login as _admin_ and password _admin_. Build a Docker image from your DAGs: 1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with `astro airflow start`. `sh mkdir my-airflow-project && cd my-airflow-project astro dev init` 2. Then build the image: `sh docker build -t my-dags:0.0.1 .` 3. Load the image into kind: `sh kind load docker-image my-dags:0.0.1` 4. Upgrade Helm deployment: sh helm upgrade airflow -n airflow --set images.airflow.repository=my-dags --set images.airflow.tag=0.0.1 astronomer/airflow Extra Objects: This chart can deploy extra Kubernetes objects (assuming the role used by Helm can manage them). For Astronomer Cloud and Enterprise, the role permissions can be found in the Commander role. yaml extraObjects: - apiVersion: batch/v1beta1 kind: CronJob metadata: name: "{{ .Release.Name }}-somejob" spec: schedule: "*/10 * * * *" concurrencyPolicy: Forbid jobTemplate: spec: template: spec: containers: - name: myjob image: ubuntu command: - echo args: - hello restartPolicy: OnFailure Contributing: Check out our contributing guide! License: Apache 2.0 with Commons Clause

OpenMeter is a real-time and scalable usage metering tool for AI, usage-based billing, infrastructure, and IoT use cases. It provides a REST API for integrations and offers client SDKs in Node.js, Python, Go, and Web. OpenMeter is licensed under the Apache 2.0 License.

The 'ai-dev-2024-ml-workshop' repository contains materials for the Deploy and Monitor ML Pipelines workshop at the AI_dev 2024 conference in Paris, focusing on deployment designs of machine learning pipelines using open-source applications and free-tier tools. It demonstrates automating data refresh and forecasting using GitHub Actions and Docker, monitoring with MLflow and YData Profiling, and setting up a monitoring dashboard with Quarto doc on GitHub Pages.

A specialist in DataOps and Data Observability, aiding in data management and monitoring.

Travel and Hospitality Industry. Hotels, Airlines, and Travel Agencies collect personal information like travel histories, passport details, and payment information, necessitating robust privacy and security measures.

Advises companies on data privacy laws, performs compliance checks, and implements data protection strategies.

Mon expert en protection des données

Compose BigQuery SQL queries for GA4 and Google Search Console.

Seismic and volcanic monitor with in-depth data and visuals.

Stay informed on AGI - with the latest, most relevant news.

Real-time Ethereum Blockchain Data & Insights (with Etherscan.io)

I analyze your workout data, focusing on brevity and clear visualizations

Frost Protection System is an AI bot optimizing open field farming of fruits, vegetables, and flowers, combining real-time data and AI to boost yield, cut costs, and foster sustainable practices in a user-friendly interface.

Explains data breaches, reasons, impacts, and identifies criminal groups.

Data and Market analysis

BNB data analyzer and predictor for PancakeSwap

Analyzes GSC data and highlights the impact of search updates.

I will create a quality data set for a personality AI, just dive into each module by saying the name of it and do so for all the modules. If you find it useful, share it to your friends

Assists conservationists in protecting endangered species by analyzing habitat data and suggesting conservation strategies.

Expert in real-time trend analysis, sourcing data-driven insights (e.g. prompt: Give me last month's top trends in AI)

Data analysis and insight tool for SMEs.

Expert in BIGA and Neapolitan pizza dough recipes, focusing on tailored calculations and precise temperature data.

companies, trends, tech and data