Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
AI systems that identify the source of cyber attacks, turning anonymous threats into actionable intelligence for policy and response.
Our AI attribution tools analyze malware code, infrastructure patterns, and attack vectors to link cyber incidents to specific threat actors with >90% confidence. This transforms raw threat data into evidence for diplomatic and retaliatory decisions.
Move from reactive defense to proactive deterrence by holding adversaries accountable.
MITRE ATT&CK frameworks and SIEM platforms.Built for national security agencies and critical infrastructure operators, our systems operate within air-gapped environments and comply with the strictest data sovereignty mandates. Learn how we harden models against adversarial attacks in our guide to AI Red Teaming and Adversarial Defense.
Move beyond detection to definitive attribution. Our AI-powered cyber attack attribution systems deliver the high-confidence intelligence needed to support decisive policy, diplomatic, and retaliatory actions against nation-state and criminal threat actors.
We deliver AI models that analyze malware code, infrastructure patterns, and TTPs to attribute attacks to specific APT groups or nation-states with quantifiable confidence scores, enabling targeted response strategies. This shifts intelligence from 'likely' to 'actionable'.
Our systems provide auditable, evidence-based intelligence trails that map attacks to geopolitical actors, supplying the technical proof points required for diplomatic engagements, sanctions, and public attribution statements.
By modeling adversary behavior and infrastructure evolution, our AI enables predictive threat hunting, identifying preparatory activity and emerging campaigns before full-scale execution, moving your posture from reactive to preemptive.
Our attribution engines are engineered for seamless integration with existing national security platforms and intelligence community data standards (e.g., STIX/TAXII, MISP), ensuring operational compatibility and streamlined analyst workflows.
Advanced clustering and anomaly detection algorithms filter out noise and irrelevant data, focusing analyst attention on high-priority, high-confidence attribution leads. This dramatically increases operational efficiency and reduces alert fatigue.
We build systems that generate court-grade forensic reports detailing the technical lineage of an attack, creating the evidentiary foundation required for legal or kinetic countermeasures under international law and rules of engagement.
Our phased methodology ensures controlled, secure, and measurable progress from initial concept to operational deployment, delivering incremental value while managing the unique risks of attribution intelligence.
| Phase & Deliverables | Phase 1: Foundation & Feasibility | Phase 2: Core Attribution Engine | Phase 3: Operational Integration |
|---|---|---|---|
Primary Objective | Validate data sources & define attribution framework | Build and validate core AI attribution models | Deploy hardened system into operational workflow |
Key Deliverables | Threat Actor TaxonomyData Pipeline ArchitectureProof-of-Concept Model | Trained Attribution ClassifierConfidence Scoring EngineAdversarial Testing Report | Production API / InterfaceIntegration with SIEM/CND ToolsOperational SOPs & Training |
Attribution Confidence | Low (Pattern Identification) | High (Group-Specific Attribution) | Very High (Actionable Intelligence) |
Model Focus | Malware code similarity & TTP clustering | Multi-modal fusion (code, infra, campaigns) | Continuous learning & real-time indicator ingestion |
Security & Compliance | Data handling protocol, air-gapped PoC environment | Model hardening, MITRE ATLAS testing, secure training | Full accreditation support, secure deployment, audit logging |
Stakeholder Engagement | Weekly technical syncs, feasibility report | Bi-weekly model review, interim intelligence briefings | Joint operational testing, final handoff & training |
Typical Duration | 4-6 weeks | 8-12 weeks | 6-10 weeks |
Starting Investment | $40K - $60K | $80K - $120K | Custom (Based on integration scope) |
Every AI attribution system we develop follows a hardened, multi-layered security framework designed to protect sensitive intelligence and ensure operational integrity in contested environments.
We enforce a mandatory Secure Development Lifecycle (SDL) for all code, integrating security gates at every phase from design to deployment. This includes threat modeling with frameworks like STRIDE and MITRE ATLAS, static/dynamic code analysis, and mandatory peer reviews to eliminate vulnerabilities before they reach production.
All development occurs within accredited, air-gapped environments with strict access controls.
We train and fine-tune attribution models exclusively within secure, sovereign compute enclaves. We implement hardware-based Trusted Execution Environments (TEEs) for sensitive inference workloads and employ secure MLOps pipelines with cryptographic signing for all model artifacts.
This ensures model integrity, prevents data poisoning, and protects the provenance of your attribution logic.
Our AI Red Teaming service is integrated into the delivery process. We conduct continuous adversarial testing against deployed models using the MITRE ATLAS framework, simulating novel attack vectors like prompt injection, model evasion, and data poisoning specific to attribution logic.
This proactive defense identifies and mitigates weaknesses before adversaries can exploit them.
We architect systems for deployment in fully air-gapped networks or sovereign cloud environments compliant with standards like FedRAMP High and IL5/6. Our designs ensure zero data exfiltration pathways, with all processing, storage, and analytics confined within your specified geopolitical boundaries.
This is critical for handling classified attack patterns and sensitive attribution findings.
We engineer immutable audit trails for every data point, model decision, and analyst interaction. Using cryptographic hashing and secure logging, we create a verifiable chain-of-custody for attribution evidence, which is essential for supporting diplomatic and policy decisions derived from AI analysis.
Our methodology is built to satisfy the strictest regulatory and compliance frameworks from day one. We design for NIST AI RMF, ISO/IEC 42001, and specific defense directives (e.g., DoD's AI Ethical Principles), embedding policy-as-code and governance controls directly into the system architecture.
Learn more about our approach to Enterprise AI Governance and Compliance Frameworks.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Technical leaders evaluating AI for cyber attack attribution need concrete answers on process, security, and outcomes. Here are the key questions we address for every engagement.
Our methodology follows a deterministic, evidence-based pipeline. We ingest malware code, network logs, and infrastructure data, then apply ensemble models including graph neural networks for TTP mapping and transformer-based code analysis. The system cross-references findings against our proprietary and commercial threat intelligence feeds to generate a confidence-scored attribution report, detailing the technical indicators linking the attack to a specific APT group or nation-state.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
