Summary‍

AI-enabled attacks and digital fraud operations are moving faster than most security programs can adapt. Threat actors are blending traditional intrusion techniques with adversarial machine learning, prompt manipulation, bot automation, and payment fraud tactics often within the same campaign.

To help organizations respond to this shift, Threatnote now integrates the following taxonomies directly into the platform as structured TTP objects:

• MITRE ATT&CK
• MITRE ATLAS
• OWASP AI & LLM security guidance
• National Institute of Standards and Technology AI Risk Management Framework
• Cisco AI Defense Taxonomy
• Stripe FT3 (Fraud Tactics, Techniques, and Tools)

These taxonomies are not simply referenced in documentation. They are operationalized inside Threatnote as STIX-native TTP objects, enabling structured alignment across detection engineering, threat hunting, fraud intelligence, and reporting.

This is a meaningful expansion of our commitment to delivering intelligence that works together.

Why This Integration Matters

Security and fraud teams are no longer operating in isolated domains.

• AI systems are being probed, poisoned, and manipulated.
• Fraud networks are leveraging automation and synthetic identities.
• Prompt injection and model evasion tactics are appearing in real-world incidents.
• Infrastructure compromise often overlaps with payment abuse or account takeover.

Historically, these behaviors have been tracked in separate frameworks and separate tools. ATT&CK covered enterprise techniques. ATLAS addressed adversarial ML. OWASP outlined AI application risks. Stripe’s FT3 taxonomy formalized fraud operations.

What has been missing is a way to unify them operationally.

Threatnote now bridges these frameworks through structured STIX modeling and cross-taxonomy alignment.

A Unified TTP Model Built on STIX

All integrated taxonomies are represented in Threatnote as STIX objects:

• attack-pattern objects for techniques
• intrusion-set objects for threat actors
• tool and malware objects where applicable
• Custom STIX extensions for AI- and fraud-specific attributes

Each TTP from ATT&CK, ATLAS, OWASP AI, NIST AI, Cisco AI Defense, and Stripe FT3 is modeled as a first-class object in the platform.

More importantly, they are aligned to one another using STIX relationships such as:

• related-to
• uses
• targets
• detects
• mitigates
• indicates

For example:

• A Stripe FT3 “card testing” fraud technique can be linked via related-to to ATT&CK credential access techniques.
• An ATLAS model evasion technique can be aligned with an OWASP LLM risk category.
• A NIST AI risk category can be connected to specific ATLAS or OWASP attack patterns through structured relationships.
• Detection signatures within Threatnote can be represented as STIX objects that detect specific attack-pattern objects.
• Threat hunts can be modeled as activities that investigate or indicate TTP usage.

Because these are structured STIX relationships, analysts can pivot across taxonomies instantly. A technique observed in fraud telemetry can be traced to related infrastructure tactics, AI manipulation patterns, and known intrusion sets.

This alignment transforms frameworks from static references into a connected intelligence graph.

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Operational Benefits for Customers

1. Cross-Domain Visibility

Customers gain a single view of:

• Traditional cyber intrusion techniques
• Adversarial machine learning behaviors
• AI application abuse patterns
• Online fraud operations
• Payment and identity exploitation tactics

Instead of siloed taxonomies, everything is normalized into one intelligence model.

2. Structured Detection Coverage

Detection signatures inside Threatnote can now be mapped directly to:

• ATT&CK techniques
• ATLAS adversarial ML techniques
• OWASP AI risk categories
• Stripe FT3 fraud techniques

Because these mappings are represented as STIX relationships, coverage can be measured across multiple frameworks simultaneously.

Customers can:

• Identify detection gaps
• Visualize technique coverage
• Correlate fraud detection with infrastructure compromise
• Demonstrate control alignment to stakeholders

This provides measurable maturity progression rather than anecdotal reporting.

3. AI Threat Hunting With Formal Technique Mapping

Threat hunts can now target:

• Model evasion patterns from ATLAS
• Prompt injection risks from OWASP AI
• Fraud automation behaviors from Stripe FT3
• Hybrid campaigns that blend AI abuse and credential compromise

Hunt findings can be linked to TTP objects through STIX relationships, allowing organizations to track recurring technique usage over time.

As more telemetry is ingested from sources like dark web monitoring, digital risk protection modules, vulnerability intelligence, and external STIX/TAXII feeds, related intelligence is automatically enriched and connected to provide broader context.

4. Threat Actor Attribution Across AI and Fraud Domains

Threat actors increasingly operate across boundaries.

An intrusion set may:

• Deploy traditional malware (ATT&CK techniques)
• Leverage AI-based phishing content
• Abuse LLM-powered automation
• Conduct synthetic identity fraud

Within Threatnote, intrusion sets are linked to all associated TTP objects through uses relationships.

Because ATLAS, OWASP AI, NIST AI, Cisco AI Defense, and Stripe FT3 techniques are represented as compatible STIX objects, actor profiling now spans AI abuse and fraud behaviors alongside traditional intrusion activity.

This provides a more complete behavioral fingerprint of adversaries.

5. Consolidated Intelligence from Multiple Sources

Threatnote consolidates TTP data from:

• Detection engineering artifacts
• Threat hunts
• Vulnerability intelligence
• Fraud monitoring feeds
• Brand protection telemetry
• Dark web credential exposure
• RSS and research ingestion pipelines
• External STIX/TAXII feeds

Each piece of intelligence is normalized into STIX objects and relationships.

As a result:

• Multiple data sources can reference the same TTP object.
• Evidence accumulates around techniques.
• Reports automatically reflect cross-source corroboration.
• Customers see how intelligence findings connect rather than appear in isolation.

This is the practical realization of intelligence that works together, with structured data flowing into a shared object model where techniques, actors, detections, and reports are all interconnected.

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A Foundation for AI-Native Security Programs

Organizations deploying AI systems must manage:

• Model risk
• Data integrity
• Prompt abuse
• Governance obligations
• Fraud exploitation
• Cross-domain adversary behaviors

By embedding leading AI and fraud taxonomies into the Threatnote intelligence fabric, customers can:

• Align AI risk frameworks to operational detections
• Map fraud tactics to cyber intrusion techniques
• Correlate AI abuse with actor attribution
• Track technique prevalence across business units
• Provide structured reporting aligned to recognized standards

All of this occurs within a unified STIX-based graph that supports pivoting, visualization, enrichment, and export.

Continuing the Expansion

The integration of ATT&CK, ATLAS, OWASP AI, NIST AI, Cisco AI Defense, and Stripe FT3 is part of a broader initiative to expand structured technique coverage across emerging domains.

As adversaries adopt AI tooling and fraud ecosystems mature, Threatnote will continue:

• Expanding taxonomy support
• Enhancing cross-framework alignment
• Deepening STIX relationship modeling
• Improving visualization of multi-taxonomy coverage
• Enabling richer export and sharing capabilities

Security programs are increasingly measured by how well they can correlate signals across domains.

By unifying AI, fraud, and traditional intrusion taxonomies within a STIX-native intelligence model, Threatnote provides customers with a structured way to operationalize that correlation.

All integrated taxonomies and newly incorporated TTPs are available within the Library section of Threatnote, under a dedicated TTP area where users can explore detailed descriptions, cross-taxonomy mappings, and framework-specific context for each technique.

That is what it means to build intelligence that works together.

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