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Flashpoint MCP Server: Operationalizing Cyber Threat Data for Agentic AI Security Workflows

In this post, we outline how cyber threat intelligence is evolving to support agentic AI-driven security operations, why MCP is emerging as a foundational standard, and how Flashpoint is operationalizing data for this new model.

Security teams are under more pressure than ever to move faster, see more, and act with confidence.

At the same time, the way cybersecurity investigations happen is evolving. The “human-in-the-loop” model is expanding: analysts increasingly direct AI agents that gather context, correlate signals across sources, and handle repetitive triage.

While AI is rapidly becoming a staple of modern security operations, a significant gap remains: most intelligence sources were originally designed for human consumption, not AI agents. Historically, threat intelligence platforms were built for analysts to log in and piece together disparate insights. While that model remains the gold standard for deep research, it can become a bottleneck in a high-velocity, agent-led workflow where AI assistants and automation pipelines are the primary investigators.

At Flashpoint, our Ignite threat intelligence platform was built to support deep investigative workflows, enabling analysts to search and connect intelligence across primary-source datasets and build a complete picture of emerging threats. That foundation remains critical.

But as workflows evolve, customers are increasingly looking to extend that same intelligence beyond the platform—into AI assistants, automation pipelines, and other environments where work is actively happening.

That raises an important question: How do you make high-value intelligence as usable for an AI agent as it is for a human analyst?

Today, we are outlining our approach to building the Flashpoint Model Context Protocol (MCP) Server, a strategic initiative that makes Flashpoint’s best-in-class intelligence accessible not only via our award-winning platform but also natively “AI-callable” within the agentic workflows of today and tomorrow.

What Is an MCP Server and Why Does It Matter in Cyber Threat Intelligence?

Model Context Protocol (MCP) is the standard for connecting AI systems to external data sources and tools. 

In practical terms, an MCP server provides a structured way for AI systems, like agents, assistants, copilots, and automation frameworks, to access and interact with data in real time.

For cyber threat intelligence, this represents a fundamental shift in how teams operate:

• Faster investigations: AI agents can query and correlate data across disparate datasets in seconds.
• Comprehensive coverage: By searching across all primary sources in parallel, teams eliminate the risk of missing critical intelligence. 
• More seamless workflows: Analysts can stay within their agentic workflow without constant context switching.
• Reduced integration overhead: Less need for custom engineering to connect intelligence into new environments.

Flashpoint MCP Server: A Foundation for AI-Native Threat Intelligence

Flashpoint has always differentiated itself on the quality and depth of our data, sourced directly from where threats emerge. Our goal is to ensure this intelligence is available wherever your analysts are working.

Currently, teams experimenting with AI assistants face significant friction: copying and pasting, relying on third-party bridges, or maintaining custom integrations.

We are building the Flashpoint MCP Server as a foundational access layer, the architectural connector that will power both external integrations and future AI experiences within the Flashpoint platform.

With this new layer, teams can:

• Query intelligence in one workflow: Access intelligence reports, ransomware, vulnerabilities, communities, and Deep Dark Web, and technical indicators in a single research task rather than hopping tool-to-tool.
• Ground AI agents in truth: Provide a direct, authenticated bridge to real-time, verified Flashpoint intelligence, ensuring AI responses are based on evidence rather than static training data or hallucinations.
• Scale expert analysis: Use guided prompts and workflow templates to teach the AI exactly how to use our tools to conduct expert-level investigations across our datasets.

The threat intelligence industry is adopting MCP as the standard for how AI systems connect to data.

We’re building the Flashpoint MCP Server to ensure our intelligence is a foundational component of that ecosystem and usable wherever AI-driven workflows occur.

What to Expect from Flashpoint MCP Server

The initial release of the Flashpoint MCP Server in Spring 2026 is intentionally read-only and query-focused. This creates the production-grade foundation required to bring intelligence into the workflows customers are already building. It aligns with customer guidance about using agentic AI to solve the most pressing challenges they face today.

What Comes Next

Later this year, we will move from information retrieval to Action-Oriented Intelligence. This expansion will allow users not only to access data but also to act on it directly within their AI-driven workflows. As this ecosystem evolves, we plan to deliver:

• Natural Language Orchestration: We are empowering analysts to interact with our data more intuitively. Through the MCP server, complex actions such as updating an investigation or identifying new threat sources are handled via natural-language orchestration. This ensures that the speed of an investigation is limited only by an analyst’s questions, not their mastery of a specific query syntax.
• Flashpoint-Native Agents and Skills: We are developing specialized Flashpoint Agents and “skills” built on top of this server. These will be purpose-built to address specific workflows, such as ransomware monitoring or vulnerability triage, allowing teams to deploy out-of-the-box expertise without building their own agentic logic
• Fusion of External and Internal Data: A critical advantage of the MCP framework is the ability to combine Flashpoint’s external threat intelligence with a customer’s internal environment data (SIEM, Cloud, IAM, Endpoint, etc.). This allows an agent to correlate global threat signals with your specific footprint to provide instant, individualized risk context. 
• Embedded AI within Flashpoint Ignite: This same MCP infrastructure will serve as the shared engine for new, embedded AI experiences within Flashpoint Ignite. This ensures that the same natural-language power and automated data correlation fueling external agents are also natively available within our platform UI, creating a seamless investigative experience regardless of where an analyst chooses to work.

Built and Validated in Real Workflows

We believe in the power of this new architecture because we are already using it. The MCP Server is currently embedded in our own Flashpoint Intelligence Team’s workflow, helping our analysts research and respond to complex client RFIs. 

By applying this capability to our own high-stakes research first, we ensure that what we bring to market is grounded in real investigative needs, not just technical potential. 

Operationalizing the Best Data

The future of security operations won’t be defined solely by who has access to the most data or even the most AI agents; it will be defined by who can operationalize the best data directly within the workflows where decisions are made.

The Flashpoint MCP Server is our strategic commitment to that future—making the world’s best intelligence natively accessible, usable, and aligned with the way modern security teams work.

The Flashpoint MCP Server is currently in active development, with customer availability planned for late Spring 2026. 

Subscribe to the Flashpoint blog for more updates on Flashpoint MCP Server and the latest insights from the front lines of threat intelligence.  

Frequently Asked Questions

What is the Flashpoint MCP Server? 

The Flashpoint MCP Server enables Flashpoint’s threat intelligence to be directly callable by AI agents. It implements the Model Context Protocol (MCP), an open standard for connecting AI systems to external data, so any MCP-compatible agent, including Claude, Gemini, and Cursor, can query our datasets without bespoke API integration work.

Who is the MCP Server designed for?

The MCP Server is designed for technical, forward-leaning security teams and AI-native organizations. This includes SOC analysts, CTI practitioners, and security engineers who are already building or experimenting with AI agent workflows using tools like Gemini, Claude Code, or custom LLM-based assistants.

Which Flashpoint datasets are accessible via MCP?

The initial rollout (Spring 2026) provides access to Flashpoint’s core intelligence collections, including:

• Intelligence Reports
• Communities (Online forums, messaging platforms, closed digital communities)
• Technical Indicators (IOCs)
• Vulnerability Intelligence (CVEs)
• Ransomware
• Compromised Credentials and Infected Hosts
• Strategic Entity Data

How does this differ from Flashpoint’s standard APIs?

While our standard APIs are designed for direct programmatic consumption, the MCP Server is optimized specifically for AI agents. It exposes intelligence as composable tools and guided prompts that AI agents can understand and use to perform complex, multi-step research tasks. 

How does this differ from the Flashpoint Ignite platform?

The Flashpoint MCP Server is not a replacement for Flashpoint’s award-winning Ignite platform; rather, it is a complementary access layer designed for a different type of user and workflow. While Ignite is a destination for deep research, the MCP server provides the infrastructure that enables that same intelligence to live in AI-native environments.

To learn more about Flashpoint’s MCP Server, schedule a demo today.

See Flashpoint in Action

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The Five Phases of the Threat Intelligence Lifecycle: A Strategic Guide

The threat intelligence lifecycle is a fundamental framework for all fraud, physical, and cybersecurity programs. It is useful whether a program is mature and sophisticated or just starting out.

What is the Core Purpose of the Threat Intelligence Lifecycle?

The threat intelligence lifecycle is a foundational framework for all fraud, physical security, and cybersecurity programs at every stage of maturity. It provides a structured way to understand how intelligence is defined, built, and applied to support real-world decisions.

At a high level, the lifecycle outlines how organizations move from questions to insight to action. Rather than focusing on tools or outputs alone, it emphasizes the practices required to produce intelligence that is relevant, timely, and trusted. This iterative, adaptable methodology consists of five stages that guide how intelligence requirements are set, how information is collected and analyzed, how insight reaches decision-makers, and how priorities are continuously refined based on feedback and changing risk conditions.

The Five Phases of the Threat Intelligence Lifecycle

Key Objectives at Each Phase of the Threat Intelligence Lifecycle

PHASE 1: Requirements & Tasking

The first phase of the threat intelligence lifecycle is arguably the most important because it defines the purpose and direction of every activity that follows. This phase focuses on clearly articulating what intelligence needs to answer and why.

As an initial step, organizations should define their intelligence requirements, often referred to as Priority Intelligence Requirements (PIRs). In public sector contexts, these may also be called Essential Elements of Information (EEIs). Regardless of terminology, the goal is the same: establish clear, stakeholder-driven questions that intelligence is expected to support.

Effective requirements are tied directly to business risk and operational outcomes. They should reflect what the organization is trying to protect, the threats of greatest concern, and the decisions intelligence is meant to inform, such as reducing operational risk, improving efficiency, or accelerating detection and response.

This process often resembles building a business case, and that’s intentional. Clearly defined requirements make it easier to align intelligence efforts with organizational priorities, establish meaningful key performance indicators (KPIs), and demonstrate the value of intelligence over time.

In many organizations, senior leadership, such as the Chief Information Security Officer (CISO or CSO), plays a key role in shaping requirements by identifying critical assets, defining risk tolerance, and setting expectations for how intelligence should support decision-making.

Key Considerations in Phase 1

PHASE 2: Collection & Discovery

The Collection & Discovery phase focuses on building visibility into the threat environments most relevant to your organization. Both the breadth and depth of collection matter. Too little visibility creates blind spots; too much unfocused data overwhelms teams with noise and false positives.

At this stage, organizations determine where and how intelligence is collected, including the types of sources monitored and the mechanisms used to adapt coverage as threats evolve. This can include visibility into phishing activity, compromised credentials, vulnerabilities and exploits, malware tooling, fraud schemes, and other adversary behaviors across open, deep, and closed environments.

Effective programs increasingly rely on Primary Source Collection, or the ability to collect intelligence directly from original sources based on defined requirements, rather than consuming static, vendor-defined feeds. This approach enables teams to monitor the environments where threats originate, coordinate, and evolve—and to adjust collection dynamically as priorities shift.

Discovery extends collection beyond static source lists. Rather than relying solely on predefined feeds, effective programs continuously identify new sources, communities, and channels as threat actors shift tactics, platforms, and coordination methods. This adaptability is critical for surfacing early indicators and upstream activity before threats materialize internally.

The processing component of this phase ensures collected data is usable. Raw inputs are normalized, structured, translated, deduplicated, and enriched so analysts can quickly assess relevance and move into analysis. Common processing activities include language translation, metadata extraction, entity normalization, and reduction of low-signal content.

Key Considerations in Phase 2

PHASE 3: Analysis & Prioritization

The Analysis & Prioritization phase focuses on transforming processed data into meaningful intelligence that supports real decisions. This is where analysts connect signals across sources, enrich raw findings with context, assess credibility and relevance, and determine why a threat matters to the organization.

Effective analysis evaluates activity, likelihood, impact, and business relevance. Analysts correlate threat actor behavior, infrastructure, vulnerabilities, and targeting patterns to understand exposure and prioritize response. This step is critical for moving from information awareness to actionable insight.

As artificial intelligence and machine learning continue to mature, they increasingly support this phase by accelerating enrichment, correlation, translation, and pattern recognition across large datasets. When applied thoughtfully, AI helps analysts scale their work and improve consistency, while human expertise remains essential for judgment, context, and prioritization especially for high-risk or ambiguous threats.

This phase delivers clarity and a defensible view of what requires attention first and why.

Key Considerations in Phase 3

PHASE 4: Dissemination & Action

Once analysis and prioritization are complete, intelligence must be delivered in a way that enables action. The Dissemination & Action phase focuses on translating finished intelligence into formats that are clear, relevant, and aligned to how different stakeholders make decisions.

This phase is dedicated to ensuring the right information reaches the right teams at the right time. Effective dissemination considers audience, urgency, and operational context, whether intelligence is supporting detection engineering, incident response, fraud prevention, vulnerability remediation, or executive decision-making.

Finished intelligence should include clear assessments, confidence levels, and recommended actions. These recommendations may inform incident response playbooks, ransomware mitigation steps, patch prioritization, fraud controls, or monitoring adjustments. The goal is to remove ambiguity and enable stakeholders to act decisively.

Ultimately, intelligence only delivers value when it drives outcomes. In this phase, stakeholders evaluate the intelligence provided and determine whether, and how, to act on it.

Key Considerations in Phase 4

PHASE 5: Feedback & Retasking

The Feedback & Retasking phase closes the intelligence lifecycle loop by ensuring intelligence remains aligned to real-world needs as threats, priorities, and business conditions change. Rather than treating intelligence delivery as an endpoint, this phase focuses on evaluating impact and continuously refining what the intelligence function is working on and why.

Once intelligence has been acted on, stakeholders assess whether it was timely, relevant, and actionable. Their feedback informs updates to requirements, collection priorities, analytic focus, and delivery methods. Mature programs use this input to adjust tasking in near real time, ensuring intelligence efforts remain focused on the threats that matter most.

Improvements at this stage often center on shortening retasking cycles, reducing low-value outputs, and strengthening alignment between intelligence producers and decision-makers. Over time, this creates a more adaptive and responsive intelligence function that evolves alongside the threat landscape.

Key Considerations in Phase 5 

Assessing Your Threat Intelligence Lifecycle in Practice

Understanding the threat intelligence lifecycle is one thing. Knowing how effectively it operates inside your organization today is another.

Most teams don’t struggle because they lack intelligence activities; they struggle because those activities aren’t consistently aligned, operationalized, or adapted as needs change. Requirements may be defined in one area, while collection, analysis, and dissemination evolve unevenly across teams like CTI, vulnerability management, fraud, or physical security.

To help organizations move from conceptual understanding to practical evaluation, Flashpoint developed the Threat Intelligence Capability Assessment.

The assessment maps directly to the lifecycle outlined above, evaluating how intelligence functions across five core dimensions:

• Requirements & Tasking – How clearly intelligence priorities are defined and tied to real business risk
• Collection & Discovery – Whether visibility is broad, deep, and adaptable as threats evolve
• Analysis & Prioritization – How effectively analysts connect signals, context, and impact
• Dissemination & Action – How intelligence reaches operations and decision-makers
• Feedback & Retasking – How frequently priorities are reviewed and adjusted

Based on responses, organizations are mapped to one of four stages—Developing, Maturing, Advanced, or Leader—reflecting how intelligence actually flows across the lifecycle today.

Teams can apply insights by function or workflow, using the results to identify where intelligence is working well, where friction exists, and where targeted changes will have the greatest impact. Each participant also receives a companion guide with practical guidance, including strategic priorities, immediate actions, and a 90-day planning framework to help translate lifecycle insight into execution.

Take the Threat Intelligence Capability Assessment to evaluate how your program aligns to the lifecycle and where to focus next.

See Flashpoint in Action

Flashpoint’s comprehensive threat intelligence platform supports intelligence teams across every phase of the threat intelligence lifecycle, from defining clear requirements and expanding visibility into relevant threat ecosystems, to analysis, prioritization, dissemination, and continuous retasking as conditions change.

Schedule a demo to see how Flashpoint delivers actionable intelligence, analyst expertise, and workflow-ready outputs that help teams identify, prioritize, and respond to threats with greater clarity and confidence—so intelligence doesn’t just inform awareness, but drives timely, measurable action across the organization.

Frequently Asked Questions (FAQs)

What are the five phases of the threat intelligence lifecycle?

The threat intelligence lifecycle consists of five repeatable phases that describe how intelligence moves from intent to action:

Requirements & Tasking, Collection & Discovery, Analysis & Prioritization, Dissemination & Action, and Feedback & Retasking.

Together, these phases ensure that intelligence is driven by real business needs, grounded in relevant visibility, enriched with context, delivered to decision-makers, and continuously refined as threats and priorities change.

Phase	Primary Objective
Requirements & Tasking	Defining intelligence priorities and tying them to real business risk
Collection & Discovery	Gathering data from relevant sources and expanding visibility as threats evolve
Analysis & Prioritization	Connecting signals, context, and impact to determine what matters most
Dissemination & Action	Delivering intelligence to operations and decision-makers in usable formats
Feedback & Retasking	Reviewing outcomes and adjusting priorities, sources, and focus over time

How do intelligence requirements guide security operations?

Intelligence requirements—often formalized as Priority Intelligence Requirements (PIRs)—define the specific questions intelligence teams must answer to support the business. They provide the north star for what to collect, analyze, and report on.

Clear requirements help teams:

• Focus: Reduce noise by prioritizing intelligence aligned to real risk
• Measure: Track whether intelligence outputs are driving decisions or action
• Align: Ensure security, fraud, physical security, and risk teams are working toward shared outcomes
  

Without clear requirements, intelligence efforts often default to reactive collection and generic reporting that struggle to deliver impact.

Why is the feedback phase of the intelligence lifecycle necessary for a proactive defense?

Feedback & Retasking turns the intelligence lifecycle from a linear process into a continuous improvement loop. It ensures intelligence stays aligned with changing threats, business priorities, and operational needs.

Through regular review and stakeholder input, teams can:

• Identify which intelligence outputs led to action and which did not
  
• Retire low-value sources or reporting formats
  
• Adjust requirements, collection, and analysis as new threats emerge

This phase is essential for moving from static reporting to intelligence-led operations, where priorities evolve in near real time and intelligence continuously improves its relevance and impact.

Blogs

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How to Combat Check Fraud: Leveraging Intelligence to Prevent Financial Loss

Criminals increasingly steal checks and sell them on illicit online marketplaces, where check fraud-related services are common. Intelligence is helping the financial sector fight back

Stolen checks and the impact of Covid-19

Checks are one of the most vulnerable legacy payment methods. Check fraud can actively affect the bottom lines (and reputations) of banks, financial services organizations, government entities, and many other organizations that utilize checks. According to the Financial Crimes Enforcement Network (FinCEN), fraud—including check fraud—is “the largest source of illicit proceeds in the US” as well as “one of the most significant money laundering threats to the United States.” 

Targeting the mail

Criminals target the US mail system to steal a variety of checks. In fact, there is a nationwide surge in check fraud schemes targeting the US mail and shipping system, as threat actors continue to steal, alter, and sell checks through illicit means and channels. 

This includes personal checks and tax refund checks to government or government assistance-related checks (Social Security payments, e.g.). Business checks are also a primary target because they are often written for larger amounts and may take longer for the victim to identify fraudulent activity.

In 2022 alone, US banks filed 680,000 check fraud-related suspicious activity reports (SARs). This represents a nearly two-fold increase from 2021 (which itself represents a 23 percent YoY increase from 2020). This surge in check fraud has been exacerbated by Covid-19 Economic Impact Payments (EIPs) under the CARES Act, which presented threat actors with a new avenue to attempt to commit fraud.

Check fraud: A mini use case 

In order to mitigate and ultimately prevent check-fraud-related risks, it’s crucial for financial intelligence and fraud teams to understand what threat actors seek, how they work, and where they operate. 

This begins, as we detail below, with intelligence into the communities, forums, and marketplaces where check fraud occurs as well as the tools that enable deep understandings, timely insights, and measurable action. 

Below is an intelligence narrative, in three acts, that tells the story of how transactions involving some of the above examples could play out.

Act I: Obtain

Threat actors are known to remove mail from individuals’ mailboxes and parcel lockers using blue box “arrow” master keys. These arrow keys are often stolen from USPS employees, which has led to numerous incidents of harassment, threats, and even violence. Generally, arrow keys are sold within illicit community chats and/or the deep and dark web, often fetching upwards of $3,000 per key.

In general, when it comes to check fraud, threat actors may sell or seek: 

• Mailbox keys
• Stolen checks
• Check alteration services (physical and digital)
• Synthetic identity provisioning
• Drop account sharing
• Counterfeit check creation
• Writing a check with insufficient funds behind it
• Insider access

Act II: Alter

Check alteration comes in two forms: “washing” and “cooking.” 

Washing refers to the process of altering a check by chemically removing ink and replacing the newly empty spaces with a different value, recipient name, or another fraud-enabling alteration. 

Cooking involves digitally scanning the check and altering text or values through digital means.

Act III: Monetize

Threat actors will deposit the fraudulent check and rapidly withdraw the funds from an ATM, or sell a stolen or altered check on an illicit marketplace or chat group, and then receive payment, often via cryptocurrency.

Four key elements of actionable check fraud intelligence

Financial institutions should rely on four essential intelligence-led technologies, tools, or capabilities to effectively combat check fraud.

1) Visibility and access to illicit communities and channels

To prevent check fraud, organizations should focus on a few key places. Financially motivated threat actors operate and share information on messaging apps like Telegram and other open-source channels, as well as illicit marketplaces on the deep and dark web. Therefore, it is imperative for financial intelligence and fraud teams to have access to the most relevant check fraud-related threats across the internet. 

Keep in mind, however, that accessing these communities is not always straightforward and, if done frivolously, can compromise an investigation.

2) Timeliness and curated alerting

Intelligence is often only as good as it is relevant. Flashpoint enables security and intelligence practitioners to bubble the most important, mission-critical intelligence through our real-time alerting capability, which allows users to receive notifications for keywords and phrases that relate to their mission, such as check fraud-related lingo and activity. 

In addition to real-time alerts, analysts can rely on curated alerting and saved searches to track topics of long-term interest. Flashpoint Ignite enables analysts to research particular accounts and their recent activity and matches transactions to their respective ATM slips and institution address. This helps to ensure the accuracy of the information found within these communities and marketplaces before raising any alarms, as many scammers post false content. 

This approach is particularly valuable as check fraudsters often share crucial information such as preferred methodologies, social media handles, and geolocations that can aid in identifying malicious activities. In addition, by closely observing newly emerging trends, such as the evolution of pandemic relief fraud to refund fraud to check fraud, analysts can proactively develop robust preventative measures to mitigate risks before these tactics become widespread.

3) Actionable OCR and Video Search

In order to provide “material proof,” cyber threat actors will often tout and post an image of a check in a chat application or marketplace in hopes of increasing the likelihood of a successful transaction. Optical Character Recognition (OCR) technology can capture important information about check fraud attempts, since actors often share images of the fraudulent check or subsequent monetization transactions. OCR alerts are customizable with the financial institution’s name and common phrases used on checks to enhance accuracy.

Images of fraudulent checks provide valuable insights into the fraud attempt, including the check’s unique identifier, the account holder’s name, the bank’s name and address, and the endorsement signature. By analyzing these details, financial institutions and law enforcement agencies can identify patterns and leads that can help them track down the perpetrators and prevent future fraudulent activity.

Moreover, ATM withdrawal slips can offer critical information about the transaction, such as the location of the ATM, the time of the deposit, and the type of account used. This data is useful when taking appropriate measures to prevent similar attempts and protect customers’ assets. With the help of advanced technologies like Flashpoint’s OCR, institutions can quickly extract and analyze this information to generate real-time alerts and take prompt action to prevent monetary losses.

An essential investigative component, Flashpoint’s industry-first video search technology, like its OCR capability, enables fraud and cyber threat intelligence (CTI) teams to surface logos, text, explicit content, and other critical intelligence to enhance investigations.

Combat check fraud with Flashpoint

Flashpoint delivers the intelligence that enables financial institutions to combat check fraud at scale. With timely, actionable, and accurate intelligence, financial institutions can mitigate and prevent financial loss, protect customer assets, and track down perpetrators. Get a free trial today to learn how:

• A financial services customer detected more than $4M in illicitly marketed assets, including checks and compromised accounts, using Flashpoint’s OCR capabilities. 

• A customer received 125 actionable alerts in a single month equated to over $15M in potentially averted losses.

• An automated alert enabled a customer to identify a threat actor’s specific operations, saving them over $5M.

Request a demo today.