AI & LLM Security in the United States

AI & LLM security in the United States protects AI and Large Language Model applications from prompt injection, data leakage, model poisoning and excessive agency. The US has no single federal AI law; the NIST AI Risk Management Framework is the de-facto standard, the NIST AI Safety Institute leads testing, and a fast-growing patchwork of state laws (e.g. the Colorado AI Act and California's rules) now imposes real obligations. CyberSigma delivers LLM red-teaming and AI governance mapped to sectoral regulators (FTC, SEC, HHS) and state attorneys general and the global frameworks (OWASP LLM Top 10, NIST AI RMF, ISO/IEC 42001, MITRE ATLAS). We are CERT-In empanelled and PCI QSA (CEMEA) authorised.

Secure AI adoption for the United States organizations

The US has no single federal AI law; the NIST AI Risk Management Framework is the de-facto standard, the NIST AI Safety Institute leads testing, and a fast-growing patchwork of state laws (e.g. the Colorado AI Act and California's rules) now imposes real obligations. That momentum means the United States organizations must now show their AI is secure, governed and compliant — not just functional.

AI introduces failure modes traditional testing misses: chatbots manipulated into leaking data, AI agents coaxed into unauthorised actions, and poisoned models or datasets from public hubs. CyberSigma secures the full AI lifecycle — model, data, application, prompts, plugins and agents — and maps every finding to sectoral regulators (FTC, SEC, HHS) and state attorneys general and recognised global frameworks.

• LLM & GenAI application penetration testing and red-teaming (OWASP LLM Top 10).
• AI/ML model, pipeline and MLOps security assessment (MITRE ATLAS, Google SAIF).
• AI governance — ISO/IEC 42001 AI Management System and NIST AI RMF.
• Local alignment with sectoral regulators (FTC, SEC, HHS) and state attorneys general.
• Secure AI adoption — GenAI usage policy, shadow-AI and data-leak controls.

The US AI compliance patchwork

Because obligations vary by state and sector, US organizations need an approach that maps controls once and satisfies many regimes — HIPAA for health AI, GLBA/SEC for financial AI, FTC unfairness/deception rules for consumer AI, and state AI acts for high-risk automated decisions. NIST AI RMF is the common backbone we build on.

What we test (OWASP Top 10 for LLMs + MITRE ATLAS)

We adversarially test your LLM and GenAI applications the way a real attacker targeting a the United States organisation would:

• Prompt injection — direct and indirect (documents, web pages, tools).
• Sensitive information disclosure — PII, secrets and system-prompt leakage.
• Insecure output handling — XSS, SSRF and code execution from model output.
• Excessive agency — agents/plugins taking unauthorised or destructive actions.
• Training-data poisoning and model/data supply-chain risks.
• Jailbreaks, guardrail bypass, model extraction and denial-of-wallet.

AI governance & compliance in the United States

We turn the applicable frameworks into a prioritised, evidenced programme:

• NIST AI RMF 1.0 — Govern, Map, Measure, Manage with a working AI risk register.
• State AI laws (e.g. Colorado AI Act) for high-risk automated decisions.
• Sectoral rules — HIPAA, GLBA/SEC, FTC — mapped to your AI use cases.
• ISO/IEC 42001 AI Management System for certifiable governance.

Is there a federal US AI law we must follow?

Not a single one. The NIST AI RMF is the recognized federal framework, while binding rules come from sector regulators (FTC, SEC, HHS) and state laws like the Colorado AI Act. We map your AI to all that apply.

What is the Colorado AI Act and does it affect us?

It is among the first US state laws regulating high-risk AI used for consequential decisions, with duties around risk management and consumer notice. If you make automated decisions about Colorado residents, it likely applies — we assess and close the gaps.

How does AI red-teaming differ from normal penetration testing?

Traditional pen testing targets code and infrastructure; AI red-teaming additionally targets the model's behaviour via prompts, poisoned context and connected tools to make it leak data or act without authorisation. Mature programmes use both — we provide each and can combine them.