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Guides on retrieval, evaluation, orchestration, and production AI delivery

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Classified Network AI Threat Detection | Inference Systems

Services

Classified Network AI Threat Detection

Deploy unsupervised machine learning and anomaly detection models engineered for air-gapped and classified networks to identify novel insider threats, sophisticated malware, and data exfiltration attempts that evade traditional signature-based security tools.

Control room desk with laptops and a large orchestration network display.

BEYOND SIGNATURES

The Signature-Based Security Gap in Classified Networks

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.

Detect novel threats: Identify zero-day malware and advanced persistent threats (APTs) without relying on known signatures or IOCs.
Identify insider risks: Model normal user and system behavior to flag anomalous data access, lateral movement, and exfiltration attempts.
Operate in air-gapped environments: Deploy and manage models within accredited, disconnected networks using secure MLOps pipelines.
Reduce false positives: Use behavioral analytics and user entity behavior analytics (UEBA) to distinguish true threats from benign anomalies, cutting alert fatigue by up to 70%.

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.

DELIVERING MISSION-CRITICAL ADVANTAGE

Operational and Strategic Benefits

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.

Proactive Insider Threat Detection

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.

> 90%

Early Detection Rate

< 50 ms

Anomaly Scoring Latency

Zero-Day & Fileless Malware Identification

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.

99.5%

Detection Accuracy

Zero

Signature Dependence

Automated Threat Triage & Response

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.

85%

Alert Volume Reduction

< 5 min

Avg. Triage Time

Continuous Compliance & Audit Readiness

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.

100%

Activity Logging

Real-time

Compliance Dashboard

Reduced Total Cost of Security Operations

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.

40-60%

Manual Effort Reduction

> 3x

Analyst Efficiency Gain

Strategic Intelligence & Adversary Modeling

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.

Learn more

A structured, risk-mitigated approach to operationalizing threat detection

Phased Deployment and Integration Timeline

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

END-TO-END SECURITY

Our Secure Development and Deployment Methodology

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.

Air-Gapped Development Environments

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.

Zero

External Connectivity

ICD 503

Accreditation Standard

Secure MLOps & Model Provenance

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.

Immutable

Audit Trail

Cryptographic

Artifact Signing

Hardened Container Deployment

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.

STIG-Compliant

Cluster Config

HSM-Integrated

Key Management

Continuous Adversarial Validation

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.

MITRE ATLAS

Testing Framework

Continuous

Feedback Loop

Confidential Computing Inference

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.

Memory Enclaves

Data Protection

Hardware-Based

TEE Integration

Compliance-Accredited Delivery

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.

Full SSP

Documentation

ATO Support

Process Guidance

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Classified Network AI Threat Detection

The Signature-Based Security Gap in Classified Networks

Operational and Strategic Benefits

Proactive Insider Threat Detection

Zero-Day & Fileless Malware Identification

Automated Threat Triage & Response

Continuous Compliance & Audit Readiness

Reduced Total Cost of Security Operations

Strategic Intelligence & Adversary Modeling

Phased Deployment and Integration Timeline

Our Secure Development and Deployment Methodology

Air-Gapped Development Environments

Secure MLOps & Model Provenance

Hardened Container Deployment

Continuous Adversarial Validation

Confidential Computing Inference

Compliance-Accredited Delivery

Frequently Asked Questions on AI Threat Detection

How long does deployment take for a classified network?

What is your methodology for detecting novel threats?

How do you ensure security and compliance during engagement?

What's included in pricing and support?

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How do you handle false positives and model drift?

What happens after the initial deployment?

Can this integrate with our existing cyber threat hunting?

Talk to the team about your AI system.