Traditional perimeter security is blind to the trusted user who has already breached your defenses. Our AI for Insider Threat Detection service fuses network logs, digital forensics, and personnel data to identify anomalous behavior indicative of espionage, sabotage, or credential theft.
Service
AI for Insider Threat Detection
Deploy behavioral analytics and UEBA models that fuse network activity, digital forensics, and personnel data to identify anomalous behavior indicative of insider threats, espionage, or compromised credentials within secure facilities.
INSIDER THREAT DETECTION
The Silent Threat Inside Your Secure Perimeter
Deploy AI-powered behavioral analytics to detect compromised credentials and malicious insiders before they cause damage.
Move from reactive logging to proactive threat hunting with User Entity Behavior Analytics (UEBA) that models normal activity and flags deviations with 99% precision, reducing false positives by over 70%.
- Continuous Risk Scoring: Automatically score user and entity risk based on fused data from
Active Directory,VPN logs,endpoint detection, andphysical access systems. - Anomaly Detection: Identify subtle threats like data hoarding, irregular access times, and lateral movement that evade rule-based tools.
- Automated Investigation: Generate prioritized alerts with correlated evidence chains, accelerating Security Operations Center (SOC) response times.
Built for secure facilities, our systems integrate with your existing Security Information and Event Management (SIEM) and support air-gapped deployments. Protect your most sensitive assets from the threat within. Explore our broader capabilities in Defense and National Intelligence AI or learn about securing AI models themselves through Adversarial AI Defense and Red Teaming.
PROACTIVE DEFENSE
Operational and Strategic Benefits
Our AI for Insider Threat Detection delivers measurable improvements in security posture and operational efficiency, moving your organization from reactive incident response to proactive risk management.
01
Proactive Risk Identification
Deploy behavioral analytics models that identify anomalous user activity indicative of espionage, sabotage, or credential compromise before a security incident occurs, shifting your defense from reactive to predictive.
> 90%
Early Detection Rate
< 1 sec
Anomaly Alert Latency
02
Reduced False Positives
Our User Entity Behavior Analytics (UEBA) models fuse network logs, digital forensics, and personnel context to dramatically reduce alert fatigue, allowing your SOC to focus on genuine threats instead of chasing noise.
60-80%
Alert Reduction
> 95%
Precision
03
Accelerated Incident Investigation
Automatically correlate disparate data points across secure facilities into a unified, auditable timeline. This provides investigators with immediate context, cutting mean time to resolution (MTTR) for insider threat cases by over 70%.
> 70%
Faster MTTR
100%
Audit Trail
04
Compliance & Audit Readiness
Generate automated reports and maintain immutable logs of all user activity and model inferences. This ensures continuous compliance with frameworks like NIST 800-53, NIST AI RMF, and ISO/IEC 27001 for security audits.
Automated
Reporting
Immutable
Data Lineage
05
Secure, Sovereign Data Processing
All models are trained and deployed within your sovereign cloud or air-gapped environment. Data never leaves your control, ensuring compliance with the strictest data residency and national security mandates.
On-Prem/Air-Gapped
Deployment
Zero Data Egress
Guarantee
06
Integration with Existing Security Stack
Our systems are engineered to integrate seamlessly with your existing SIEM (e.g., Splunk, Elastic), IAM, and endpoint protection platforms, enhancing your current investments without requiring a full infrastructure overhaul.
REST APIs
Integration
< 4 weeks
Typical Integration
A Structured, Risk-Mitigated Approach
Phased Engagement & Deliverables
Our proven methodology for deploying AI for Insider Threat Detection, designed to deliver rapid value while ensuring security and compliance at every stage.
| Phase & Deliverables | Discovery & Assessment (Weeks 1-2) | Prototype & Validation (Weeks 3-6) | Pilot Deployment (Weeks 7-12) | Enterprise Rollout & Scale (Ongoing) |
|---|---|---|---|---|
Core Objective | Define threat taxonomy & data access | Validate detection logic on sample data | Live monitoring in controlled environment | Full-scale, automated UEBA system |
Key Activities | Stakeholder interviewsData source inventoryCompliance gap analysis | Behavioral model designAnomaly scoring prototypeInitial false positive tuning | Integration with SIEM/SOARLimited user group monitoringSLA & performance baselining | Multi-site deploymentContinuous model retrainingIntegration with HR/Physical security systems |
Primary Deliverable | Comprehensive Threat Assessment & Architecture Plan | Working Prototype with Initial Detection Metrics | Pilot Report: Efficacy, ROI, & Operational Plan | Fully Operational System with 24/7 Managed Support |
Data Processing Scope | Metadata analysis only | Anonymized historical data sample (< 6 months) | Live data from 1-2 high-risk departments | Enterprise-wide data fusion (network, endpoint, physical access) |
Security & Compliance Review | Gap analysis against NIST AI RMF, ISO 42001 | Model card & bias assessment | Full security audit & penetration test | Automated compliance reporting & audit trail |
Success Metrics Defined | Threat coverage matrix, data readiness score | Detection accuracy (>85%), false positive rate (<10%) | Mean time to detect (MTTD) reduction, user acceptance | Quantified risk reduction, operational efficiency gains |
Inference Systems Support | Dedicated Solution Architect & Security Lead | AI Engineering Team (2-3 engineers) | Dedicated MLOps Engineer & Security Analyst | Dedicated Account Manager & 24/7 SOC Integration |
Client Commitment | Stakeholder access & data governance approval | Feedback on prototype & labeling assistance | Pilot environment provisioning & operational feedback | Ongoing governance committee participation |
Typical Investment | $15K - $25K | $45K - $75K | $80K - $150K | Custom Annual Subscription (SLA-based) |
TARGETED DEPLOYMENT
Primary Use Cases & Industries
Our insider threat detection AI is engineered for high-stakes environments where data sovereignty, behavioral nuance, and adversarial resilience are non-negotiable. We deliver systems that move beyond simple rule-based alerts to predictive, context-aware risk scoring.
01
Classified Network & Secure Facility Monitoring
Deploy unsupervised machine learning models within air-gapped or highly restricted networks to detect subtle anomalies in user behavior, data access patterns, and privileged account usage that indicate potential espionage or credential compromise. Our systems are designed for accredited environments and integrate with existing SIEM and DLP tools.
Key Differentiator: Models are trained and deployed within secure enclaves, with no external data exfiltration, ensuring compliance with the strictest data sovereignty mandates like ICD 503 and NIST SP 800-53.
Air-Gapped
Deployment Model
ICD 503
Compliance Framework
02
Defense Industrial Base & Contractor Security
Protect Controlled Unclassified Information (CUI) and ITAR/EAR-regulated data across contractor networks. Our UEBA models fuse digital forensics from endpoints, network activity logs, and physical access data to identify employees or subcontractors exhibiting risky behavior indicative of intellectual property theft or preparation for data exfiltration.
Key Differentiator: Integration with project management and source code repositories (e.g., GitLab, Jira) provides context-aware risk scoring, distinguishing between legitimate work and suspicious data aggregation.
CUI/ITAR
Data Focus
Multi-Source
Data Fusion
03
Financial Services & Trading Floor Surveillance
Mitigate insider trading, fraud, and data leakage risks within investment banks, hedge funds, and exchanges. Our AI analyzes communication patterns (email, chat), trading system access, and market data queries in real-time to flag potential front-running, information barrier breaches, or collusion.
Key Differentiator: Models are tuned for the high-velocity, high-stakes environment of finance, with sub-second latency for real-time alerting and integration with compliance platforms like Actimize or NICE.
< 1 sec
Alert Latency
FINRA/SEC
Regulatory Alignment
04
Critical Infrastructure & Energy Sector
Secure Operational Technology (OT) and Industrial Control Systems (ICS) against insider-enabled sabotage. Our models monitor engineer workstation activity, command sequences sent to PLCs/SCADA systems, and anomalous network traffic between IT and OT zones to detect malicious intent or compromised credentials that could lead to physical disruption.
Key Differentiator: Specialized understanding of OT protocols (e.g., Modbus, DNP3) and failure modes, reducing false positives from legitimate engineering work while catching subtle, multi-stage attack preparations.
IT/OT Fusion
Monitoring Scope
NERC CIP
Compliance Support
05
Pharmaceutical & Biotech R&D Protection
Safeguard billion-dollar intellectual property in drug discovery and clinical trials. Our AI establishes behavioral baselines for researchers and lab personnel, detecting unusual data downloads, access to competitor-related files, or attempts to exfiltrate sensitive genomic or chemical compound data outside approved channels.
Key Differentiator: Ability to process and analyze unstructured data from lab notebooks, scientific PDFs, and genomic databases to contextualize user actions within the complex R&D workflow.
Unstructured Data
Analysis Capability
21 CFR Part 11
Audit Trail Support
06
Government Agencies & Intelligence Community
Implement robust User Entity Behavior Analytics (UEBA) for personnel with high-level security clearances. Our systems correlate data from polygraph cycles, financial disclosures, foreign travel reports, and digital activity to provide continuous evaluation and flag potential indicators of coercion, financial distress, or ideological shift that could precede espionage.
Key Differentiator: Designed for integration with legacy government systems and adherence to Intelligence Community Directive (ICD) standards, providing explainable AI outputs suitable for adjudicative review.
Continuous Eval
Personnel Focus
ICD 704
Policy Alignment
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Talk to Us
Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Enterprise searchRAGPermissions
Read more
Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
AI agentsWorkflow automationGovernance
Read more
Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
AI integrationDecision supportModel routing
Read moreAI for Insider Threat Detection
Frequently Asked Questions
Get specific answers about deploying behavioral analytics and UEBA models to secure your most sensitive environments.
We follow a proven 5-phase methodology: 1) Threat Modeling & Data Mapping to identify critical assets and data sources, 2) Secure Data Pipeline Engineering for ingesting network logs, DLP alerts, and HR data, 3) Model Selection & Training using specialized UEBA algorithms on your historical data, 4) Controlled Pilot Deployment with a 99.5% precision threshold to minimize false positives, and 5) Full Integration & Analyst Training. This structured approach, refined across 50+ secure facility projects, ensures reliable detection of credential misuse, data exfiltration, and anomalous user behavior.
About the author
Prasad Kumkar
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.
LinkedIn
Limited slotsGet a Free AI Consultation
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
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.
01
Review the use case
We understand the task, the users, and where AI can actually help.
Read more02
Pick the right approach
We define what needs search, automation, or product integration.
Read more03
Build the first useful version
We implement the part that proves the value first.
Read more04
Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
Talk to Us