Commercialized and fielded DoD-funded SBIR research into innovative binary vulnerability analysis features:

• Analyzes compiled binary software automatically using a range of techniques
• Symbolic execution flagship feature analyzes binaries in-depth to find what other tools often miss.
• Supports a wide range of OSs and CPU architectures, and range of SAST and other analyses
• Funded by Navy, DARPA, Army, DTRA  — fully productized and fielded as BinLens   → BinLens

Commercialized USAF & NIST funded SBIR research for innovative vulnerability analyses for LLMs and traditional models.

• Analyzes traditional models (e.g. computer vision) using novel adversarial attacks & synthetic/real data
• Layerwise white-box analysis to determine root causes of issues to allow rapid patching
• Analyze LLMs whitebox for malicious prompt vulnerabilities (proof-of-concept feature)
• Built on DoD/NIST-funded research and commercialized and productized into FortiLayer   → FortiLayer

DTRA-funded beta release of an agentic, conversational AI orchestration platform for software vulnerability analysis.

• Conversational interface for analyzing binaries, source code, and Java bytecode
• Agentic backend performs novel analyses and orchestrates multiple tools
• Enables analysts to ask questions, explore findings, and iterate interactively
• Built on DoD-funded research for high-assurance security workflows

Navy-funded SBIR R&D (“Char5G”) developing automated methods to detect, analyze, and characterize vulnerabilities across 5G/NR mission environments.

• Analyzes 5G/NR protocols, components, and deployments for exploitable weaknesses
• Automates vulnerability characterization for rapid, repeatable security assessments
• Integrates with mission workflows to strengthen 5G-enabled operations and critical communications
• Designed for defense, national security, and other high-assurance 5G environments

Navy-funded R&D (“SCRAMS”) transforms procurement data into supply-chain risk insights, Air Force-funded R&D (“AFMEP”) adds circuit-board–level analysis..

• Ingests SAP, Navy ERP, spreadsheets procurement data and BOM exports into a graph database for analysis
• Detects risk indicators and anomalies that may signal counterfeit, tampered, or high-risk components
• Allows drill-down search, relationship mapping, and visualization to expose hidden vulnerabilities
• Outputs automated analytics and reports to support risk mitigation.

NIST-funded R&D (“NCT”) to automatically score cyber-hygiene of IT/OT/ICS vendors and products  — with the goal to incentivize both vendors and buyers.

• Normalized cybersecurity score to vendors based on exploitability, remediability, severity, and transparency
• Uses only publicly-available data sources to compute risk — enabling transparency
• Highlights weaknesses in vendor cyber-hygiene to incentivize improved security practices and reduce risk
• Produces defensible, auditable scores to support compliance, acquisition, and mission assurance workflows

NIST/DoD/EU-funded R&D advancing model-driven security policy generation and testing, and consistent, automated technical access-control enforcement.

• Generates security and access-control policies automatically from high-level models
• Ensures consistent enforcement across distributed, heterogeneous systems
• Reduces configuration errors and policy drift in mission-critical environments
• Supports defense, federal, and industrial systems requiring precise, auditable security controls

Air Force-funded R&D (“TAV-SCRAMS”) developed “DCP”, a no/low-code tool that cleans dirty/inconsistent datasets — ideal for legacy data migration, analytics & AI/ML pipelines.

• Supports ingestion from disparate sources — databases, spreadsheets, data lakes, legacy systems
• Detects and corrects formatting errors, duplicates, missing fields, outliers, and inconsistent entries
• Offers intuitive low-code UI for defining cleansing rules, using condition-based logic
• Includes configurable node-sequences, anomaly detection, and ML-based data classification