Latest news with #ModelContextProtocol
Business Wire
4 hours ago
- Business
- Business Wire
Skyflow Unveils MCP Data Security for Enterprises and SaaS Companies
PALO ALTO, Calif.--(BUSINESS WIRE)--Enterprise AI agents need access to customer data to deliver value. But each MCP connection adds a security risk. As the Model Context Protocol (MCP) becomes the backbone of agentic AI systems, Skyflow is launching its MCP Data Protection Layer—purpose-built for SaaS platforms and enterprises adopting MCP. Enterprise AI agents need customer data to deliver value, but every MCP connection creates potential security risk. Skyflow's new MCP Data Protection Layer solves this challenge, enabling secure AI deployment without compromising functionality. Share MCP standard was introduced by Anthropic, and is now supported by OpenAI, AWS, and Google. It streamlines how AI agents connect securely to real-world tools like databases, SaaS tools and apps, etc. without writing custom code. But this introduces a new risk frontier: sensitive data like PII, PHI, and financial records flowing through the MCP servers without proper safeguards. Skyflow Addresses Sensitive Data Protection Challenges with MCP Use Unlike traditional DLP tools that simply block data, Skyflow takes a more intelligent approach. Its unique polymorphic data protection engine dynamically transforms sensitive information in real time by masking, tokenizing, or rehydrating fields based on policy and user permissions. This ensures security and compliance without breaking AI agent functionality. Skyflow MCP Data Protection is available in two deployment models: Skyflow MCP Gateway: A proxy layer, which can be integrated into existing proxy servers that sits between MCP servers or agents and backend data sources, enforcing field-level privacy policies without requiring application changes. Skyflow MCP Server SDK: An embeddable library that developers can use to build privacy controls directly into MCP server implementations and agentic apps. Both options include enterprise-grade privacy features: Use case-aware redaction and de-identification Entity-preserving transformations for agent reasoning Contextual rehydration for authorized users Secure memory handling to prevent PII retention Full audit trails for GDPR, HIPAA, and other regulatory compliance 'As AI agents start connecting to more real-world data through MCP, companies need privacy infrastructure that can keep up,' said Anshu Sharma, CEO of Skyflow. 'Skyflow helps developers and SaaS platforms protect sensitive data without slowing down AI workflows—making secure, compliant AI deployment possible at scale.' Enterprises and SaaS companies across retail, financial services, healthcare, travel and hospitality can deploy Skyflow's MCP protection to enable AI agents with sensitive data access while maintaining regulatory compliance. Today's MCP Data Protection offering announcement extends Skyflow's AI security roadmap. It builds on the earlier launch of Agentic AI Security and Privacy Layer launched last year, and the GPT Privacy Vault introduced in 2023. To understand the specific privacy risks MCP servers introduce, read our detailed blog post: Building Secure AI Agent Architecture with Model Context Protocol About Skyflow Skyflow is the security and privacy platform for the modern AI data stack built to radically simplify how companies isolate, protect, and govern their customers' most sensitive data. With its Data Privacy Vault, Skyflow enables businesses to store, process, and share sensitive data securely. Leading investors back Skyflow, and the company is trusted by Fortune 500 and growth companies across financial services, healthcare, travel & hospitality, and retail.
Mint
3 days ago
- Mint
Perplexity macOS app now supports Anthropic's MCP for system tasks: What it does and how you can use it
Perplexity now works more like a personal helper than a chatbot. The company has added support for Anthropic's Model Context Protocol (MCP), a framework that lets AI tools connect with system level apps and services on your device. This change allows Perplexity to do more than respond to questions. It can check your Apple Calendar, add reminders, create notes, or even look up files from your Google Drive. Instead of jumping between tabs or digging through menus, you can now ask Perplexity to handle small but important tasks and it will respond inside the chat window, just like a human assistant would. Before diving further, let's look at what MCP actually is. MCP short form for Model Context Protocol is a system designed by Anthropic to give AI assistants a way to work with apps securely and in context. That means the assistant does not just respond to your questions anymore, it can now interact with your actual apps, with permission. Think of it this way. Instead of explaining what is on your calendar, you can let the assistant check it directly. Rather than typing a to do list into your notes, you can just ask it to add the tasks for you. MCP allows this by creating safe connections between the AI and your apps. It helps turn conversations into real actions while keeping you in control and protecting your privacy. Let's break this down. Suppose you ask Perplexity to do something that involves an app, like creating a calendar event or retrieving a file, it checks to see if it has access to that app through MCP. If it does have, then the action is completed inside the chat. If it does not, it will ask you to approve the connection. For example, if you say, 'Add a note saying check client email,' the assistant will prompt you to enable Notes access. Once allowed, it handles the task and confirms within the chat thread. It gives you updates right away and walks you through each step. There is no guessing, no complicated setup, and no silent background activity. Perplexity's integration with MCP brings several new tools to the table. They're small changes, but they genuinely help with things you'd normally do manually. Connects with system apps Perplexity can now easily access Apple Notes, Reminders, Calendar, and more. It can add reminders, send emails, or summarise a message in simple words. Works with online storage If you use Google Drive, the assistant can search, preview, or pull up documents as needed, saving time and reducing clicks. Because of MCP, Perplexity understands what you are asking in relation to your tools. If you say, 'Remind me to email the report,' it can place that task in your Reminders app without needing a full command. No actions without approval The app will always ask for your permission before accessing any system or cloud service. Nothing is connected unless you say so. Your permission matters here. Getting started with MCP on the Perplexity Mac app is very simple, but there are a few key steps to follow. Here's how you can set it up: 1) Install the Perplexity/XPC helper app This is needed to run the MCP server. 2) Open Perplexity settings Go to your account, click on Connectors, then select Add Connector. In the Simple tab, choose MCP Connector and give it any server name. Copy the command from the MCP server README and paste it in the command box. 5) Install any needed tools Follow the README to install requirements. Perplexity may also help with this. Click Save. Make sure the MCP server shows as Running in the connector list. Go back to the homepage and turn on MCP under Sources. Instead of just giving answers, Perplexity can now actually take action on your behalf. It interacts with your apps directly, which means fewer clicks, faster responses, and less switching between tools. It is not meant to replace everything just yet, but it shows that assistants like Perplexity are starting to move beyond the browser and into your real workflow. For many users, this change could lead to a simpler way of handling daily digital tasks.
Toronto Star
4 days ago
- Business
- Toronto Star
Audiense Launches MCP Connectors: Bringing Audience Intelligence into your AI Workflow
Fort Worth, TX, July 28, 2025 (GLOBE NEWSWIRE) — FOR IMMEDIATE RELEASE Audiense launches MCP Connectors: Bringing Audience Intelligence into your AI Workflow Fort Worth, TX – July 28, 2025 — Audiense, the leading audience intelligence platform, today unveiled its MCP (Model Context Protocol) connectors – seamless integrations that bring audience insights directly into AI tools like Claude and ChatGPT. This marks a major leap forward in how marketing, research, and strategy teams work with audience insights – making them accessible, actionable, and customizable, without a single line of code. ARTICLE CONTINUES BELOW Bringing Audience Intelligence into the AI Ecosystem The new MCP connectors act as real-time bridges between all Audiense consumer intelligence products (including Insights, SOPRISM, Demand, and Tweet Binder) and today's leading generative AI platforms. Instead of pulling data manually or scanning through insights, users can interact with audience data directly within the AI tools they're already using and generate outputs like: Persona summaries Persona summaries Segment comparisons Persona summaries Segment comparisons Full-funnel campaign strategies Persona summaries Segment comparisons Full-funnel campaign strategies Content calendars Persona summaries Segment comparisons Full-funnel campaign strategies Content calendars White space opportunities Creative concepts and briefs All using simple prompts, bringing audience intelligence into their everyday workflows, instantly and intuitively. 'AI isn't just a productivity hack; it's becoming the new interface for how brands engage with their audiences. With MCP, we're embedding Audiense insights directly into that workflow, empowering users to interact with their audience segments in real-time, uncover deeper understanding, and shape strategy without friction. It's not just about working faster, it's about building smarter, more dynamic conversations with the people that matter most.' - Jim Swift, CEO of Audiense Built For How You Work Whether you're shaping strategy or executing creative, Audiense's MCP connectors are designed to support the way modern teams operate, helping them move faster, think deeper, and create better. For Marketers: Build campaign strategies, messaging frameworks, and creative briefs on the fly. For Analysts: Compare audience segments, spot trends, and test hypotheses without touching code. ARTICLE CONTINUES BELOW ARTICLE CONTINUES BELOW For Strategists: Uncover white space, surface opportunities, and turn audience insight into competitive advantage. For Content & Creative Teams: Generate content calendars, brainstorm ideas, and adapt tone of voice to each persona. 'This isn't just about saving time. It's about possibility. Whether you're writing a brief, testing a hypothesis, or developing an agent, MCP connectors give you a faster, more flexible way to turn insight into action.' - Javier Burón, Product Strategist at Audiense Rethinking How You Work with Insights MCP connectors are designed for speed, flexibility, and real-world applications – redefining how teams interact with insights and deliver outcomes. What sets MCP apart: Fully customizable: Ask any question, your way Fully customizable: Ask any question, your way Platform-neutral: Pull insights from across Audiense products Fully customizable: Ask any question, your way Platform-neutral: Pull insights from across Audiense products No-code: Built for everyone, not just technical teams Fully customizable: Ask any question, your way Platform-neutral: Pull insights from across Audiense products No-code: Built for everyone, not just technical teams Outcome-driven: Get briefs, slides, summaries and strategies, not just data Fully customizable: Ask any question, your way Platform-neutral: Pull insights from across Audiense products No-code: Built for everyone, not just technical teams Outcome-driven: Get briefs, slides, summaries and strategies, not just data Secure by design: Authenticate through your existing credentials and navigate data just as you would, safely and seamlessly Interoperable: Connect with other MCP connectors to build smart workflows –like instantly turning a report summary into a Canva presentation This launch reaffirms Audiense's commitment to innovation and to building the future of audience intelligence alongside the evolution of AI. To learn more about the MCP connectors, read our latest blog or request a demo. ARTICLE CONTINUES BELOW ARTICLE CONTINUES BELOW About Audiense Audiense is an AI-powered suite of solutions that helps brands transform complex customer data into meaningful insights and measurable outcomes. Backed by PSG Equity, Audiense combines capabilities from location intelligence, ecommerce analytics, and real-time audience insights to deliver a holistic view of customer behavior, motivation, and intent. Built for strategy, marketing, and growth teams, Audiense empowers brands to engage the right people online and in person, and to personalize every interaction with clarity and precision. Media Contact: Saman Bhatti VP, Growth saman@
Hamilton Spectator
4 days ago
- Business
- Hamilton Spectator
Audiense Launches MCP Connectors: Bringing Audience Intelligence into your AI Workflow
Fort Worth, TX, July 28, 2025 (GLOBE NEWSWIRE) — FOR IMMEDIATE RELEASE Audiense launches MCP Connectors: Bringing Audience Intelligence into your AI Workflow Fort Worth, TX – July 28, 2025 — Audiense, the leading audience intelligence platform, today unveiled its MCP (Model Context Protocol) connectors – seamless integrations that bring audience insights directly into AI tools like Claude and ChatGPT. This marks a major leap forward in how marketing, research, and strategy teams work with audience insights – making them accessible, actionable, and customizable, without a single line of code. Bringing Audience Intelligence into the AI Ecosystem The new MCP connectors act as real-time bridges between all Audiense consumer intelligence products (including Insights, SOPRISM, Demand, and Tweet Binder) and today's leading generative AI platforms. Instead of pulling data manually or scanning through insights, users can interact with audience data directly within the AI tools they're already using and generate outputs like: All using simple prompts, bringing audience intelligence into their everyday workflows, instantly and intuitively. 'AI isn't just a productivity hack; it's becoming the new interface for how brands engage with their audiences. With MCP, we're embedding Audiense insights directly into that workflow, empowering users to interact with their audience segments in real-time, uncover deeper understanding, and shape strategy without friction. It's not just about working faster, it's about building smarter, more dynamic conversations with the people that matter most.' - Jim Swift, CEO of Audiense Built For How You Work Whether you're shaping strategy or executing creative, Audiense's MCP connectors are designed to support the way modern teams operate, helping them move faster, think deeper, and create better. For Marketers: Build campaign strategies, messaging frameworks, and creative briefs on the fly. For Analysts: Compare audience segments, spot trends, and test hypotheses without touching code. For Strategists: Uncover white space, surface opportunities, and turn audience insight into competitive advantage. For Content & Creative Teams: Generate content calendars, brainstorm ideas, and adapt tone of voice to each persona. 'This isn't just about saving time. It's about possibility. Whether you're writing a brief, testing a hypothesis, or developing an agent, MCP connectors give you a faster, more flexible way to turn insight into action.' - Javier Burón, Product Strategist at Audiense Rethinking How You Work with Insights MCP connectors are designed for speed, flexibility, and real-world applications – redefining how teams interact with insights and deliver outcomes. What sets MCP apart: This launch reaffirms Audiense's commitment to innovation and to building the future of audience intelligence alongside the evolution of AI. To learn more about the MCP connectors, read our latest blog or request a demo . About Audiense Audiense is an AI-powered suite of solutions that helps brands transform complex customer data into meaningful insights and measurable outcomes. Backed by PSG Equity, Audiense combines capabilities from location intelligence, ecommerce analytics, and real-time audience insights to deliver a holistic view of customer behavior, motivation, and intent. Built for strategy, marketing, and growth teams, Audiense empowers brands to engage the right people online and in person, and to personalize every interaction with clarity and precision. Media Contact: Saman Bhatti VP, Growth saman@
USA Today
5 days ago
- Business
- USA Today
MCP Connects, SDP Delivers: The Missing Half of AI Memory is Here
Prescott, Arizona / Syndication Cloud / July 22, 2025 / David Bynon Key Takeaways Model Context Protocol (MCP) creates AI connections to external tools but doesn't define structured memory content Semantic Digest Protocol (SDP) provides trust-scored, fragment-level memory objects for reliable AI operations Multi-agent systems typically fail due to missing shared, verifiable context rather than communication issues MCP and SDP together form a complete memory architecture that stops hallucinations and contextual drift MedicareWire will implement SDP in 2025 as the first major deployment of AI-readable, trust-verified memory in a regulated domain AI's Memory Crisis: Why Today's Systems Can't Remember What Matters Today's AI systems face a critical problem: they process vast information but struggle with reliable memory. This isn't merely a technical issue — it's what causes hallucinations, inconsistency, and unreliability in advanced AI deployments. This problem becomes obvious in multi-agent systems. When specialized AI agents work together, they don't typically fail from poor communication. They fail because they lack shared, scoped, and verifiable context. Without standardized memory architecture, agents lose alignment, reference inconsistent information, and produce unreliable results. David Bynon, founder at MedicareWire, identified this issue early on. In regulated areas like Medicare, incorrect information can seriously impact consumers making healthcare decisions. The solution needs two protocols working together to create a complete memory system for AI. The first protocol, Model Context Protocol (MCP), addresses the connection problem. But it's just half of what's needed for truly reliable AI memory. Understanding Model Context Protocol (MCP) IBM recently recognized the Model Context Protocol (MCP) as core infrastructure for AI systems, describing it as 'USB-C for AI' — a universal connector standard allowing AI models to connect with external tools, data sources, and memory systems. This recognition confirmed what many AI engineers already understood: standardized connections between AI models and external resources build reliable systems at scale. IBM's Recognition: The 'USB-C for AI' Breakthrough The USB-C comparison makes sense. Before USB standardization, connecting devices to computers required numerous proprietary ports and cables. Before MCP, every AI tool integration needed custom code, fragile connections, and ongoing maintenance. IBM's official support of MCP acknowledged that AI's future requires standardized interfaces. Just as USB-C connects any compatible device to any compatible port, MCP creates a standard protocol for AI systems to interact with external tools and data sources. What MCP Solves: The Transport Problem MCP handles the transport problem in AI systems. It standardizes how an AI agent: Negotiates with external systems about needed information Creates secure, reliable connections to tools and data sources Exchanges information in predictable, consistent formats Maintains state across interactions with various resources This standardization allows developers to build tools once for use with any MCP-compliant AI system. Custom integrations for each new model or tool become unnecessary — just consistent connectivity across platforms. The Critical Gap: Missing Content Definition Despite its value, MCP has a major limitation: it defines how AI systems connect, but not what the content should look like. This resembles standardizing a USB port without defining the data format flowing through it. This creates a significant gap in AI memory architecture. While MCP handles connections, it doesn't address: How to structure memory for machine understanding How to encode and verify trust and provenance How to scope and contextualize content How information fragments should relate to each other This explains why AI systems with excellent tool integration still struggle with reliable memory — they have connections but lack content structure for trustworthy recall. Semantic Digest Protocol: The Memory Layer MCP Needs This is where the Semantic Digest Protocol (SDP) fits — built to work with MCP while solving what it leaves unaddressed: defining what memory should actually look like. Trust-Scored Fragment-Level Memory Architecture SDP organizes memory at the fragment level, instead of treating entire documents as single information units. Each fragment — a fact, definition, statistic, or constraint — exists as an independent memory object with its own metadata. These memory objects contain: The actual information content A trust score based on source credibility Complete provenance data showing information origin Scope parameters showing where and when the information applies Contextual relationships to other memory fragments This detailed approach fixes a basic problem: AI systems must know not just what a fact is, but how much to trust it, where it came from, when it applies, and how it connects to other information. Using the 'USB-C for AI' analogy, SDP is a universal, USB-C thumb drive for the Model Context Protocol. It provides data, across multiple surfaces, in a format MCP recognizes and understands Machine-Ingestible Templates in Multiple Formats SDP creates a complete trust payload system with templates in multiple formats: JSON-LD for structured data interchange TTL (Turtle) for RDF graph representations Markdown for lightweight documentation HTML templates for web publication Invented by David Bynon as a solution for MedicareWire, the format flexibility makes SDP work immediately with existing systems while adding the necessary trust layer. For regulated sectors like healthcare, where MedicareWire operates, this trust layer changes AI interactions from educated guesses to verified responses. The Complete AI Memory Loop: MCP + SDP in Action When MCP and SDP work together, they form a complete memory architecture for AI systems. Here's the workflow: From User Query to Trust-Verified Response The process starts with a user query. Example: 'What's the Maximum Out-of-Pocket limit for this Medicare Advantage plan in Los Angeles?' The AI model uses MCP to negotiate context with external resources. It identifies what specific plan information it needs and establishes connections to retrieve that data. The external resource sends back an SDP-formatted response with the requested information. This includes the MOOP value, geographic scope (Los Angeles County), temporal validity (2025), and provenance (directly from CMS data), all with appropriate trust scores. With trust-verified information, the model answers accurately: 'The 2025 Maximum Out-of-Pocket limit for this plan in Los Angeles County is $4,200, according to CMS data.' No hallucination. No vague references. No outdated information. Just verified, scoped, trust-scored memory through standardized connections. Eliminating Hallucinations Through Verified Memory This method addresses what causes hallucinations in AI systems. Rather than relying on statistical patterns from training, the AI retrieves specific, verified information with full context about reliability and applicability. When information changes, there's no need to retrain the model. The external memory layer updates, and the AI immediately accesses new information—complete with trust scoring and provenance tracking. Real-World Implementation: MedicareWire 2025 This isn't theoretical — SDP launches on in August 2025, marking the first major implementation of AI-readable, trust-scored memory in a regulated domain. 1. First Large-Scale Deployment in a Regulated Domain The healthcare industry, especially Medicare, offers an ideal testing ground for trust-verified AI memory. Incorrect information has serious consequences, regulations are complex, and consumers need reliable guidance through a confusing system. MedicareWire's implementation will give AI systems unprecedented accuracy when accessing Medicare plan information. Instead of using potentially outdated training data, AI systems can query MedicareWire's SDP-enabled content for current, verified information about Medicare plans, benefits, and regulations. 2. Solving Healthcare's Critical Information Accuracy Problem Consumers using AI assistants for Medicare options will get consistent, accurate information regardless of which system they use. The SDP implementation ensures any AI agent can retrieve precise details about: Plan coverage specifications Geographic availability Cost structures and limitations Enrollment periods and deadlines Regulatory requirements and exceptions All come with proper attribution, scope, and trust scoring. 3. Creating the Foundation for Multi-Agent Trust Infrastructure Beyond immediate benefits for Medicare consumers, this implementation creates a blueprint for trust infrastructure in other regulated fields. Multi-agent systems will have shared, verifiable context — eliminating drift and hallucination problems that affect complex AI deployments. The combination of MCP's standardized connections and SDP's trust-verified memory builds the foundation for reliable AI systems that can safely operate in highly regulated environments. From Connection to Memory: The Future of Reliable AI Is Here David Bynon, founder of Trust Publishing and architect of SDP, states: 'We didn't just create a format. We created the trust language AI systems can finally understand — and remember.' As AI shapes important decisions in healthcare, finance, legal, and other critical fields, reliable, verifiable memory becomes essential. The MCP+SDP combination shifts from probabilistic guessing to trust-verified information retrieval — defining the next generation of AI applications. SDP will be available as an open protocol for non-directory systems, supporting broad adoption and continued development across the AI ecosystem. As the first major implementation, MedicareWire's deployment marks the beginning of a new phase in trustworthy artificial intelligence. MedicareWire is leading development of trustworthy AI memory systems that help consumers access accurate healthcare information when they need it most. David Bynon 101 W Goodwin St # 2487 Prescott Arizona 86303 United States
