Security for AI.
From silicon to system.
AI agents are gaining physical form — controlling robots, vehicles, and critical infrastructure. AGIACC builds the security infrastructure that makes AI safe to deploy, from pure software hardening through confidential computing to hardware-enforced capability architectures.
Building on proven security foundations
Three layers of AI security. Software to silicon.
AI Runtime Hardening
SafeClaw — our secure runtime layer for AI agents. Plugin isolation, prompt injection detection, PII redaction, tamper-evident audit logging, and human approval gates. Deploy AI agents safely without hardware changes, with immediate protection against the most common attack vectors.
TEE-Protected AI Workloads
Trusted Execution Environments for AI inference and training. Model weights remain encrypted even during computation. CPU TEEs (Intel TDX, AMD SEV-SNP) combined with GPU TEEs (NVIDIA Confidential Computing) protect models, data, and credentials from infrastructure operators and attackers alike.
CHERI Capability Architecture
Deterministic memory safety and fine-grained compartmentalization enforced directly in silicon. CHERI replaces raw pointers with unforgeable, bounded capability tokens — stopping buffer overflows, use-after-free, and privilege escalation at the processor level with near-zero performance overhead.
SafeClaw: making AI agent deployments trustworthy.
OpenClaw showed what happens when AI gets hands
In 2026, the OpenClaw AI agent system — capable of controlling hardware, executing shell commands, and accessing messaging platforms — demonstrated the true cost of building powerful capabilities on insecure foundations.
CVE-2026-25253 enabled one-click remote code execution. 800+ malicious plugins infiltrated ClawHub. 135,000+ instances were publicly exposed online, leaking API keys, credentials, and chat histories. Meta banned it internally. Chinese regulators issued formal warnings.
Software patches can't fix architectural insecurity. The runtime itself must change.
SafeClaw architecture
Plugin isolation — Each agent tool runs in a hardware-compartmentalized sandbox. A malicious plugin cannot read memory outside its allocation; violations trap instantly at the CPU level.
Confidential execution — Sensitive operations run inside TEEs. API keys, model weights, and user data stay encrypted even during processing. Verified by cryptographic attestation.
Verifiable runtime — EQTY-Lab-style silicon-based enforcement and attestation prove the execution environment hasn't been tampered with, at near-zero performance cost.
Defense in depth — Prompt injection detection, PII redaction, human approval gates, and tamper-evident audit logging — layered from software through hardware.
AI security is the defining infrastructure challenge of the decade.
As AI systems gain autonomy, physical form, and regulatory exposure, the value of security-first infrastructure compounds. Every autonomous system — from factory robots to self-driving vehicles — will need a trusted runtime layer. We build it.
We don't just need AI for everything. We need trustworthy AI for everything.
Deep infrastructure security, built to be investable.
Ready to secure your AI infrastructure?
Whether you're deploying AI agents, building autonomous systems, or evaluating trusted computing for your stack — we should talk.