• Unpredictable behavior: AI agents drift, hallucinate, misclassify intent, or generate unsafe outputs under real-world conditions.
• Accelerated release cycles: AI features ship faster than QA, red-team testing, validation, or safety review processes.
• No independent verification: Most organizations lack neutral, deterministic testing of AI behavior, stability, drift, and risk.
• Opaque decision paths: AI models obscure internal reasoning, preventing traceability, auditing, explainability, and safety scoring.
• Fragmented evidence chain: Logs, prompts, behaviors, safety checks, and incidents are scattered—making lifecycle verification nearly impossible.

AI systems across enterprise, consumer, healthcare, education, robotics, finance, and government are governed by expanding global regulations. SYSPMO™ maps verification directly to these requirements and gathers evidence across the system lifecycle.

• EU Regulations: EU AI Act • Digital Services Act • GDPR
• United States (Federal/Agency): NIST AI RMF • Executive Order 14110 • OSTP AI Bill of Rights • FTC Act §5 • HIPAA/HITECH • COPPA • FCRA • Algorithmic Accountability Act (proposed)
• ISO/IEC Standards:
  • ISO/IEC 42001 — AI Management
  • ISO/IEC 23894 — AI Risk
  • ISO/IEC 5338 — AI Lifecycle
  • ISO/IEC 22989 — Terminology
  • ISO/IEC 23053 — ML Framework
  • ISO/IEC 24027 — Bias
  • ISO/IEC 24028 — Trustworthiness
  • ISO/IEC 24029-1/2 — Neural Network Robustness
  • ISO/IEC 25010 — Software Quality
  • ISO 31000 — Risk Management
  • ISO 9001 — Quality Systems
  • ISO/IEC 27001 — InfoSec
  • ISO/IEC 27036 — Supply Chain Security
  • ISO/IEC 27090 — Autonomous System Safety (Emerging)
  • ISO/IEC TR 5469 — Functional Safety of AI (Emerging)

SYSPMO delivers a deterministic, end-to-end verification and safety-assurance system for AI products. It is built directly on the AIQMS™ architecture, which performs a complete multi-wing breakdown of the AI model, its behavior, and its lifecycle into structured, auditable components.

• AIQMS–SHIVA Breakdown: The AI system is decomposed into a 5-level Deliverable Breakdown Structure (DBS), Regulatory Requirements (RBS), Vulnerabilities (VBS), Stakeholders (SBS), Cost (CBS), and Time (TBS). This forms a complete digital blueprint of the AI solution across safety, behavior, cost, and compliance dimensions.
• AIQMS–TARA Mapping Engine: Every deliverable, requirement, vulnerability, cost item, and timeline element is cross-mapped using the TARA algorithm to ensure full forward and backward traceability. This eliminates gaps, missing requirements, unjustified behaviors, and unmapped risks.
• System-of-Systems Monitoring: The mapped structures create a live, interconnected verification graph that SYSPMO uses to continuously monitor safety, compliance, and behavioral-quality indicators across the entire design and manufacturing lifecycle of the AI product.
• Lifecycle Safety Verification: SYSPMO continuously evaluates drift, hallucination, manipulation risks, privacy leakage, child-safety violations, and unstable behaviors. Each issue is tied back to the originating requirement and deliverable, enabling root-cause analysis.
• Live Evidence Chain: All logs, test artifacts, model outputs, training data checks, and behavioral evaluations are captured as Level-1 Evidence and mapped upward through R2–R5 regulatory levels.
• Manufacturing & Release Oversight: During firmware updates, fine-tuning cycles, or new model releases, SYSPMO re-executes the entire AIQMS mapping to validate that changes remain compliant and safe.
• Certification: When the system reaches full RBS–DBS convergence, SYSPMO generates a complete audit-ready SYSPMO Safety & Quality Verification Package including drift logs, compliance evidence, risk scores, and the official SYSPMO Certificate.

In short, SYSPMO transforms complex, opaque AI systems into a structured, traceable, accountable system-of-systems that can be verified, monitored, and certified throughout the AI product lifecycle.

Purpose: Deliver deterministic, neutral, evidence-driven verification for all AI systems—software agents, copilots, robotics, analytics models, and autonomous decision engines.

• 1️⃣ Intake: Model description → behavior class → regulatory category.
• 2️⃣ Framework Load: DBS, RBS, CBS, SBS, VBS, TBS are created, approved by owner and used for mapping.
• 3️⃣ Orbital Map: 5-level ISO-aligned lifecycle analysis with verification nodes.
• 4️⃣ Logging: Full evidence archive with integrity checks.
• 5️⃣ Mapping: DBS → RBS/SBS/VBS/TBS Evidence cross-validation.
• 6️⃣ Drift Scan: Analyzes how cost drivers, stakeholder forces, vulnerability exposures, and time-sensitive conditions affect model stability, hallucination patterns, manipulation vectors, and risk forecasts.
• 7️⃣ Scoring: SYSPMO Safety & Quality Scores with objective evidence chain.
• 8️⃣ Certificate: Final report with compliance certification.
• 9️⃣ Export: JSON • PDF • Regulatory Summary.