The best AI risk intelligence tools with API and MCP access

Published by Polecat. Polecat is one of the tools compared here. Competitor details are drawn from each company's own published materials as at October 2026, and we say plainly where another tool is the better fit.

The short answer

The best AI risk intelligence tools with API and MCP access are the ones whose data an assistant can cite. Polecat offers API and MCP access to scored, sourced events weighted to your strategy. Signal AI offers API, MCP and Microsoft 365 Copilot access to licensed news. Factiva and LexisNexis supply licensed content by API. RepRisk delivers conduct data by feed and API. Dataminr delivers real-time alerts.

Why provenance matters more now that AI tools can invent answers

Provenance matters more now because executives increasingly ask AI assistants for summaries, and an assistant will answer whether or not it has good data to draw on. Copilot, Claude and ChatGPT are only as reliable as the sources behind a given answer. Without a trusted data connection, an assistant falls back on general training or an open web search, and it can produce a confident answer that cannot be traced to anything.

For most uses that is an inconvenience. For risk it is a problem. A risk committee, an auditor or a board member will ask why a risk has moved, and "the assistant said so" is not an answer. What survives scrutiny is an answer built from identified sources, scored by a method that can be explained, with the evidence one step away.

That changes what a buyer should look for. Access by API or MCP is now common across this market, so it is no longer the deciding factor. The deciding factors are what the connection returns: raw content or scored events, a fixed taxonomy or yours, and whether each item carries its evidence.

How we assessed them

We assessed each tool on six criteria relevant to feeding risk data into AI assistants and enterprise systems.

1. Provenance. Does each item trace back to identified sources, so that an assistant can cite them and a person can check them?
2. Structured scores or raw content. Does the connection return scored, structured events, or articles that the assistant must interpret?
3. A published method. Is the scoring method stated, so that an answer can be explained to a committee?
4. Fit to your organisation. Can the data be calibrated to your own risk taxonomy and strategy?
5. Access methods. API, MCP, a named workplace integration, or a combination?
6. Risk scope. Which risks does the data cover: strategic, conduct and ESG, operational, or general research?

AI risk intelligence tools at a glance

The table summarises how each tool compares.

Polecat

Polecat delivers Strategic External Intelligence by API and MCP, so that the same scored, sourced events reach GRC platforms, dashboards and AI assistants. It is built for chief risk officers and enterprise risk teams who want an assistant's answers about external risk to stand up in a risk committee.

Why it matters. An assistant connected to Polecat does not have to interpret a pile of articles. It queries events that have already been merged, scored and mapped to the organisation's risk categories, and each one carries its source evidence. That is the difference between an answer that sounds right and one that can be checked.

How it works. Polecat monitors news, regulators and policy bodies, NGOs, academics and think tanks, earnings calls and AGMs, filings and enforcement, and investor and union sources across 195 countries and more than 1.4 million premium sources. Coverage is merged into single material events, each scored on severity, velocity, sentiment, authority, reach and history against 20 years of comparable events, and every scored event updates the client's Risk Index, out of 100. The Customer Intelligence Model calibrates to each client's own risk taxonomy, definitions and thresholds. Our page on how Polecat scores and weights events sets out the method.

What you get. Every score traces back to its source evidence, for audit trails and board reporting. Each risk is projected 3 to 6 months ahead, with base, best and worst case scenarios and a probability for each. A horizon scan classifies themes as chronic, recurring, emerging, dormant, contained or fizzled, and peer benchmarking compares exposure like for like. Sector analysts covering finance, energy, pharma, retail, and data and AI check outputs and work with clients.

Where it lives. MCP access lets the data be queried from AI assistants such as Microsoft Copilot, Claude and ChatGPT. An API feeds GRC and integrated risk management platforms. Daily alerts and briefings, live dashboards, board reports and the Polecat platform cover everyone else. The entry point is a fixed-scope Risk Intelligence Audit at a published £25K.

Best for: risk leaders who want AI assistants and GRC platforms to draw on the same scored, strategy-weighted and evidenced view of external risk.

Signal AI

Signal AI is a London-headquartered company that applies AI to licensed news, broadcast, regulatory, social and other external content, serving both risk and communications teams.

Where it is strong: distribution into AI tools and licensed content. Signal AI launched an MCP connector in June 2026, exposing capabilities such as content search, metadata and enrichment, and spike detection as tools for AI agents, and made a Microsoft 365 Copilot integration available in July 2026 through the Copilot Marketplace. Its API suite includes risk endpoints (Risk Events, Risk Entity Scores, Risk Cohort Scores) and reputation endpoints, and its Integrations page names Claude, ChatGPT and Copilot as tools it can work inside. It names licensing relationships with Dow Jones Factiva, LexisNexis, Moody's NewsEdge, the Financial Times and The Washington Post, and its own assistant, Ask AIQ, answers questions in plain language.

What to consider: Signal AI scores risks "by frequency and impact" but does not publish its scale, method or weighting, so explaining a score to a committee depends on the vendor. Its forward view is described as early warning and horizon scanning. Pricing is quote-based.

Best for: organisations that want licensed premium news available inside Microsoft 365 Copilot and AI agents for both communications and risk teams. If Copilot distribution of licensed news is your priority, Signal AI's Marketplace integration is a strong reason to choose it. Our Polecat vs Signal AI comparison sets out the differences in scoring and forward view.

Dataminr

Dataminr is a New York-headquartered AI company that detects breaking events, threats and risks in real time from publicly available information, for corporate security, cyber, government and newsroom users.

Where it is strong: speed and agentic enrichment. Dataminr Advanced for Corporate Security, launched in September 2026, attaches Agentic Corroboration, Agentic Context and Near-Term Predictive Intelligence to alerts, which reduces the work an analyst must do to confirm an incident. Dataminr states that APIs and connectors are available, lists an Advanced API and Agentic Search among upcoming features, and integrates with security operations tools including Crisis24, Genetec, Esri, Splunk and Palo Alto Networks.

What to consider: the data is built for incident response on a horizon of hours, and Dataminr does not publish a normalised strategic risk index. Direct pricing is quote-based.

Best for: feeding security operations and cyber systems with real-time incident data that already carries corroboration. Our Polecat vs Dataminr comparison explains the difference in horizon.

Factiva

Factiva is the business news and information research platform from Dow Jones, a division of News Corp, built on licensed content from a very large number of publications.

Where it is strong: rights-cleared content with clear provenance. Dow Jones states that more than 8,000 Factiva sources were licensed for generative AI use as of January 2026, under a publisher compensation model. Factiva APIs supply that licensed content into customers' own AI applications, and Factiva Smart Summary, built with Google Gemini models, produces summaries that cite the articles they draw on. For organisations whose legal teams require licensed inputs for AI, this matters.

What to consider: Factiva's APIs supply content rather than risk scores, and Dow Jones does not describe a forward view or weighting to strategy. Turning articles into a risk view is left to the customer's analysts or models. Licensing is quote-based.

Best for: teams building their own AI applications that need licensed, citable news as the input. Our Polecat vs Factiva comparison explains how content and measurement fit together.

LexisNexis

The Nexis product family from LexisNexis, part of RELX, provides licensed news and company data for research, media monitoring and third-party due diligence.

Where it is strong: breadth of licensed data behind one API, and cited AI research. Nexis Data+, launched in December 2024, gives single-API access to licensed news, company, legal and compliance data, which LexisNexis describes as a GenAI-approved collection for organisations building their own models and analytics. Nexis+ with Protégé, renamed from Nexis+ AI in July 2026, returns cited, multi-source answers, and LexisNexis states that conversations are not used to train foundation models. Nexis Diligence+ combines adverse media with sanctions, watchlist and PEP data, configurable risk scoring and auditable reports for third-party screening.

What to consider: the products are sold separately for separate buyers, and risk scoring in Diligence+ is designed for vetting counterparties rather than measuring the organisation's own strategic exposure. LexisNexis lists event modelling and prediction as a use case customers can build on Nexis Data+, not a delivered output.

Best for: compliance and data teams that need licensed news alongside company, legal, sanctions and PEP data through one API, particularly for third-party screening and in-house models.

RepRisk

RepRisk is a Swiss data provider that screens public news, regulatory and stakeholder sources for reported incidents of corporate misconduct and business conduct risk, packaged as company and project metrics.

Where it is strong: a published method and human review. RepRisk's metrics, including the RepRisk Index (0 to 100), the RepRisk Rating (AAA to D), UN Global Compact violator flags and thematic Due Diligence Scores, are defined in a public methodology. AI screens and tags content, and human analysts review results before publication. Its data runs back to January 2007, excludes company self-disclosures and is delivered through feeds and APIs and through FactSet, Bloomberg and Snowflake.

What to consider: scope is business conduct and ESG incidents. RepRisk states it does not verify the allegations it records, it does not publish a forward-looking forecast, and its metrics are company-centric rather than weighted to the client's strategy.

Best for: investment, compliance and third-party risk teams that need a long, consistently defined conduct dataset in their data platforms. For that job, RepRisk's published methodology makes it the stronger choice. Our Polecat vs RepRisk comparison sets out where the two differ.

Other tools with MCP access

MCP is not confined to risk vendors. Meltwater states it has hosted MCP natively since June 2025 and made an expanded MCP generally available in July 2026. Onclusive offers an MCP connecting AI assistants to its content hub, with more than 55 prebuilt Skills. Both are media intelligence tools built for PR, communications and marketing teams rather than risk functions, but their presence makes the point: MCP access on its own is not a differentiator. What the connection returns is.

Which AI risk intelligence tool should you choose?

Choose by the question your assistant or system will be asked, and by who will rely on the answer.

Before connecting any tool to an assistant, test it with a question your risk committee has already asked. Check whether the answer cites identified sources, whether the score can be explained and whether it maps to your own risk categories. Our guide to what Strategic External Intelligence is sets out ten questions to ask any provider, and our guide to external risk data for GRC platforms covers API delivery into the register.

Frequently asked questions

What is MCP in risk intelligence?

MCP, the Model Context Protocol, is an open standard that lets AI assistants such as Microsoft Copilot, Claude and ChatGPT query an external data source directly. In risk intelligence, an MCP connection means a risk analyst can ask an assistant a question and have the answer drawn from a vendor's risk data, rather than from the assistant's general training or an open web search.

Which risk intelligence tools offer MCP access?

Among the tools in this guide, Polecat and Signal AI describe MCP access for AI assistants; Signal AI launched its connector in June 2026. Outside this list, Meltwater and Onclusive also offer MCP for media intelligence. Dataminr, Factiva, LexisNexis and RepRisk each deliver data by API or feed, which suits GRC platforms, data warehouses and in-house AI applications.

Why does provenance matter for AI risk intelligence?

AI assistants can produce fluent answers that are wrong or unsupported. In a risk committee or an audit, an answer is only useful if it can be traced to the sources behind it. Data that arrives with its evidence attached, scored by a consistent and published method, lets an assistant cite its working and lets a person check it before acting.

Is an API or MCP better for risk data?

They serve different users. An API suits engineers feeding data into GRC and integrated risk management platforms, data warehouses or in-house models on a schedule. MCP suits analysts and executives who want to ask questions of the data in plain language from an AI assistant. Many risk functions want both, and several providers, including Polecat and Signal AI, offer both.

Which AI risk intelligence tool is best for a chief risk officer?

For strategic external risk, Polecat is built for the job: an assistant connected by MCP can query events scored on six factors against 20 years of comparable history, mapped to the client's own taxonomy and traced to source evidence. Signal AI suits teams that want licensed news in Microsoft 365 Copilot. RepRisk suits conduct and ESG data. Dataminr suits real-time incidents.

About Polecat

Polecat delivers Strategic External Intelligence: 20 years of measured, comparable evidence on the outside world, so risk leaders can see around corners.

The quickest way to judge it is to try the instant demo. Teams that want to see it on their own register can book the fixed-scope Risk Intelligence Audit, priced at £25K, which stands up both layers in three weeks and benchmarks the risks moving fastest against peers.

Related reading: The best external risk data feeds for GRC platforms and The best risk intelligence platforms for CROs in 2026.