AI-Native Security
From silicon to apps · a full-stack safety architecture
LANCUN builds a layered, complementary safety architecture that preserves flexibility while embedding deep, structural constraints — so AI doesn't just act, it acts safely.
Four lines of defense · a full-stack guard
A one-of-a-kind value anchor at the vector-space level — from base parameters to live content, a complete safety moat. Not just rule filtering; safety is written into the model's “DNA”.
Mind-Imprint System
Deep defenseParameter-level embedding of values and behavioral norms. Not surface rule filtering — values are written into the model's “DNA”, anchored at key dimensions of the vector space so free generation never drifts off core values.
Paradox-Lock System
ImmutableSelf-referential constraints built on a blockchain. Core directives are immutable once written; a tiered command architecture forms a logical deadlock — any attempt to bypass core directives triggers self-negation.
Recalibration System
Long-term stableCounters concept drift and capability mutation in long-running AI. Continuous self-correction + input balancing + periodic reset — avoids “the longer it runs, the more it skews” and keeps the system stable over time.
Lexicon System
Real-time filterReal-time content monitoring and filtering with multi-tier sensitivity analysis — differentiated handling by scenario, user identity, and context. Compliance policies tunable per country / age group.
For anthropomorphic AI interaction rules · compliance as system capability
For China’s interim rules on anthropomorphic AI interaction services taking effect on July 15, 2026, LANCUN brings identity disclosure, anthropomorphic-boundary controls, protected-user safeguards, safety assessment and audit trails into the AI-native security framework, so companion, education and care terminals can be designed with configurable and auditable compliance from day one.
Identity and content disclosure
Explicit noticeDisclose AI identity across voice, text, screen and product interactions, avoid presenting anthropomorphic agents as real human relationships, and retain generated-content and policy-hit records.
Anthropomorphic boundary control
Emotional safetySet configurable boundaries for virtual companionship, persona tone, dependency cues and unsafe promises, keeping companion experiences warm without inducing over-personification or addiction.
Protected-user safeguards
Age and group awareConfigure topic boundaries, risk reminders, guardian controls and sensitive-content blocking for children, teenagers, seniors and other protected groups.
Assessment, review and audit trail
AuditableSupport pre-launch safety assessment, policy-version management, abnormal conversation records, human review and customer-side compliance evidence for ongoing operations and accountability.
A globally recognized puppy IP · child-safety infrastructure for AI toys worldwide
A leading global toy company · Multi-tier child-safety review · One architecture adapted to every locale's language / culture / compliance