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Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Deploy unsupervised AI to detect novel insider threats and sophisticated malware that bypass traditional tools.
Signature-based tools are blind to novel threats. We engineer unsupervised machine learning models specifically for air-gapped and classified environments to identify anomalous behavior indicative of data exfiltration, zero-day malware, and insider risks that evade known patterns.
Our approach integrates with your existing security stack, providing a proactive detection layer that shifts your posture from reactive to predictive. Learn more about our broader capabilities in Defense and National Intelligence AI and Preemptive Cybersecurity and Threat Intelligence AI.
Our Classified Network AI Threat Detection service delivers measurable improvements in security posture, operational efficiency, and strategic intelligence. We move beyond reactive alerts to provide proactive, autonomous protection for your most sensitive environments.
Deploy unsupervised machine learning models that establish behavioral baselines for every user and device on your classified network. Our systems identify subtle anomalies indicative of credential misuse, data hoarding, or preparatory exfiltration activity—often weeks before a traditional security tool would generate an alert. This shifts your defense from reactive investigation to preemptive intervention.
Our models analyze process behavior, memory allocation, and network call patterns to detect novel malware and living-off-the-land techniques that bypass signature-based antivirus. By focusing on malicious behavior rather than known file hashes, we identify and contain threats that have never been seen before, closing a critical gap in air-gapped network defense.
Reduce analyst cognitive overload with AI-driven prioritization and automated initial response. Our system correlates low-level anomalies into high-fidelity incidents, provides explainable reasoning for each alert, and can execute pre-approved containment actions like session termination or network segmentation. This accelerates mean time to respond (MTTR) and allows your team to focus on strategic analysis.
Maintain continuous compliance with frameworks like NIST SP 800-53, NIST AI RMF, and ICD 503. Our platform provides immutable audit logs of all AI model decisions, user activities, and automated responses. Generate compliance reports on-demand and demonstrate due diligence in protecting classified data, streamlining your accreditation and inspection processes.
Achieve significant operational savings by automating routine detection and response tasks. Our clients typically see a 40-60% reduction in manual alert investigation hours. The system's precision reduces false positives, allowing your most experienced personnel to focus on advanced threat hunting and strategic security architecture, maximizing the return on your security investment.
Transform raw network data into strategic intelligence. Our AI doesn't just find threats—it models adversary tactics, techniques, and procedures (TTPs). Gain insights into potential attack campaigns, understand your network's unique attack surface, and receive actionable recommendations for hardening defenses. This moves your security program from a cost center to a source of strategic advantage. Learn more about our approach to AI-Driven Cyber Threat Hunting.
Our proven methodology for deploying unsupervised ML threat detection on air-gapped networks, ensuring minimal operational disruption and maximum security posture improvement.
| Phase | Key Activities | Duration | Outcome Milestone |
|---|---|---|---|
Phase 1: Discovery & Network Mapping | Asset inventory, traffic baseline analysis, policy review | 2-3 weeks | Comprehensive network topology and behavioral baseline established |
Phase 2: Pilot Model Deployment | Deploy lightweight anomaly detection agents on non-critical segments | 3-4 weeks | Initial threat model validated; false positive rate below 5% |
Phase 3: Full-Scale Sensor Rollout | Agent deployment across all critical network tiers and data egress points | 4-6 weeks | 100% coverage of high-value assets and data flows |
Phase 4: Model Tuning & Analyst Integration | Fine-tune detection thresholds, integrate alerts into SOC workflow | 2-3 weeks | SOC analysts achieve 90%+ efficiency in triaging AI-generated alerts |
Phase 5: Continuous Learning & Threat Hunting | Enable unsupervised model retraining, establish proactive hunting protocols | Ongoing | System autonomously detects novel attack patterns; mean time to detection (MTTD) reduced by 70% |
Security Accreditation Support | Documentation for ATO, STIG compliance, and continuous monitoring | Parallel to all phases | Full accreditation package delivered for Authority to Operate (ATO) |
Integration with Existing SIEM/SOAR | API-based integration with Splunk, Elastic, IBM QRadar, etc. | Phase 3-4 | Unified alerting and automated response playbooks enabled |
We engineer AI threat detection systems with security as the foundational layer, not an afterthought. Our methodology is built on accredited frameworks and zero-trust principles to ensure your classified networks remain protected from development through to operational deployment.
All model training, fine-tuning, and testing is conducted within physically isolated, accredited secure development facilities. This eliminates external attack vectors during the most vulnerable phase of the AI lifecycle, ensuring no sensitive data or model weights ever touch an internet-connected system.
We utilize hardware-segmented workstations and follow ICD 503/CNSSI 1253 controls for high-assurance systems.
We implement hardened MLOps pipelines with cryptographic signing for every model artifact, container, and configuration. Full lineage tracking—from training data version to final deployment hash—is maintained on an immutable ledger, providing auditable proof of model integrity and compliance with data handling mandates.
Models are packaged as minimal, signed containers with all unnecessary libraries and services stripped out. We enforce strict seccomp and AppArmor profiles, non-root execution, and integrate with hardware security modules (HSMs) for key management. Deployment is orchestrated via secure, on-premise Kubernetes clusters configured to DISA STIG benchmarks.
Our deployment includes integrated red teaming using the MITRE ATLAS framework. We simulate advanced persistent threats (APTs) and data poisoning attacks against the live system to validate detection efficacy and resilience. Findings are fed back into model retraining cycles, creating a continuous feedback loop for security hardening.
For the highest sensitivity workloads, we deploy models within hardware-based Trusted Execution Environments (TEEs) like Intel SGX or AMD SEV. This ensures data and the model itself are encrypted in memory during inference, protecting against insider threats and sophisticated memory-scraping attacks even on compromised hosts.
We deliver a complete accreditation package, including System Security Plans (SSP), Continuous Monitoring (ConMon) strategies, and all necessary documentation for Authority to Operate (ATO) under frameworks like RMF, DIACAP, or your agency-specific guidelines. Our engineers support your security team through the entire accreditation process.
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.
Get specific answers on deployment, security, and operational details for our classified network threat detection service.
Typical deployment for a hardened, air-gapped environment is 4-6 weeks. This includes secure hardware provisioning, on-premise model installation, baseline behavior profiling, and integration with your existing SIEM (e.g., Splunk, Elastic). For networks with pre-existing accredited infrastructure, timelines can be reduced to 2-3 weeks.
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.
