AGNI Modules
AGNI is composed of focused modules, each designed to address a specific category of failure commonly observed in Web3 and financial infrastructure. Rather than attempting to automate security broadly, each module targets a narrow problem where continuous visibility meaningfully improves outcomes.
BugScan AI
Modern platforms expose an increasingly complex attack surface — web interfaces, APIs, automation layers, smart contracts, and third-party integrations all evolve continuously. Manual testing alone cannot keep pace, while automated scanners often overwhelm teams with low-value alerts.
BugScan AI sits between those extremes.
It continuously checks defined surfaces using a hybrid detection approach. Known vulnerability patterns are identified through structured rulesets (including web and API security standards), while machine-assisted analysis highlights unusual configurations or behavioral inconsistencies. Signals are correlated to remove duplicates and elevate genuinely exploitable paths.
Findings that require interpretation — for example logic abuse or architectural implications — are intentionally escalated for human review rather than automatically classified.
BugScan AI therefore maintains baseline security hygiene between audits without attempting to replace adversarial testing.
Typical users
Security teams, internal red teams, and engineering teams managing large or frequently changing environments.
Status Early development — currently used internally to support engagements. External access planned after validation.
PoR Analyzer
Proof-of-Reserves disclosures often improve perception but not confidence. Assets may be visible, yet liabilities remain unclear, wallet coverage incomplete, and monitoring static.
The PoR Analyzer focuses on whether transparency mechanisms actually communicate solvency signals.
It aggregates on-chain wallet activity together with disclosure information and operational inputs. The system evaluates asset distribution, detects concentration risk, tracks balance consistency, and surfaces anomalies that merit investigation.
Importantly, the module does not attempt to certify solvency or replace auditors. Instead, it highlights confidence indicators and operational risks so that human reviewers can interpret them in context.
Typical users
Exchanges, custodians, brokers, and compliance teams responsible for transparency initiatives.
Status MVP in progress — architecture complete, pilots planned alongside live engagements.
ISO Compliance Engine
Security frameworks frequently fail because they are treated as documentation projects rather than operational systems. Teams collect evidence for audits but struggle to translate requirements into daily practice.
The ISO Compliance Engine maps formal controls to practical workflows.
It identifies gaps between expected and actual procedures, assigns responsibility, and tracks evidence as it is produced. The goal is not certification itself, but readiness — ensuring processes exist and function before an auditor arrives.
Final certification decisions remain external. The module exists to make preparation real rather than performative.
Typical users
Security leadership, compliance teams, and growing platforms preparing for regulatory or enterprise requirements.
Status Planned — design complete, implementation scheduled after PoR stabilization.
Threat Monitoring Dashboard
Many incidents are not missed because data is unavailable, but because signals are fragmented and difficult to interpret. Teams either receive no warning or receive too many.
The Threat Monitoring Dashboard aggregates operational signals and provides assisted situational awareness.
It establishes behavioral baselines across activity sources, detects meaningful deviations, and correlates events to reduce false positives. The system intentionally stops short of automated response — interpretation and action remain with operators.
The goal is clarity, not autonomy.
Typical users
Security operations teams, infrastructure operators, and exchange environments requiring operational awareness.
Status Planned — to follow core module stabilization.
Why the Modular Structure Matters
Each module addresses a specific failure pattern observed in real incidents. They are deliberately constrained in scope and integrate human review by design.
This approach allows Decrypt0 to expand coverage gradually without introducing false certainty — maintaining accountability while improving visibility.
👉 Next: Security & Standards — the methodologies guiding all AGNI modules
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