Security remains the #1 barrier to deploying agentic AI in production. This paper explores the risks holding enterprises back and how a Rust-based execution core with Python accessibility creates a safer foundation for adoption.
Enterprises cite consistent concerns
Cybersecurity threats
Data privacy risks
Lack of regulation
Missing internal policies
“Enterprises won't scale agentic AI without trust. By embedding security at the systems level, GraphBit ensures agents can be deployed in production with confidence.”
CEO
InfinitiBit
Built-in secret management, policy rules, and validation to prevent gaps.
Scoped tokens, session integrity, and fail-fast authentication mechanisms.
Safe templates, strict input checks, and protected routes by default.
Principle of least privilege with no unintended data sharing.
Audit-ready reporting with hooks for GDPR, HIPAA, and SOC 2 compliance.
CVE scans, static analysis, and leaked-secret detection.
See how GraphBit's built-in security features solve the most critical threats facing AI deployments today.
Rising threats of data breaches and system vulnerabilities
Challenges in protecting sensitive information while maintaining functionality
Lack of comprehensive frameworks for AI governance and compliance
Difficulties in establishing and enforcing consistent AI security policies
Without secure foundations, agentic AI adoption carries real risks across industries. GraphBit addresses these risks with security built in—not patched on.
Enhanced risk management, fraud detection, and regulatory compliance in banking and fintech.
Secure patient data processing, diagnostic accuracy, and HIPAA compliance in medical AI.
Safety-critical system reliability, real-time decision making, and regulatory compliance.
This white paper introduces the first open-source agentic AI framework designed with security by default, extensibility by design. For enterprises, it means safer adoption, lower compliance overhead, and greater confidence in production.
