Founded in 2024
Anyreach, Inc. recognises that the confidentiality, integrity and availability of information and data created, maintained and hosted by us are vital to the success of the business and privacy of our partners.
As a service provider/product, we understand the importance in providing clear information about our security practices, tools, resources and responsibilities within Anyreach, Inc. so that our customers can feel confident in choosing us as a trusted provider.
This Security Posture highlights high-level details about our steps to identify and mitigate risks, implement best practices, and continuously develop ways to improve.
Founded in 2024
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Compliances (11)
Here are the compliance frameworks that Anyreach, Inc. follows which showcases our adherence to industry-standard security guidelines and practices.
HIPAA
Certified
Compliant with HIPAA requirements
SOC 2
Certified
SOC 2 is a voluntary compliance standard for service organizations, developed by the American Institute of CPAs (AICPA), which specifies how organizations should manage customer data. The standard is based on the following Trust Services Criteria: security, availability, processing integrity, confidentiality, and privacy.
AIGP
Compliant
AI certification
The IAPP Certified AI Governance Professional (AIGP) Body of Knowledge (v2.1, effective February 2, 2026) is the definitive framework for assessing competency in AI governance across four domains: foundational AI governance principles (responsible AI, organizational readiness, cross-functional collaboration), applicable laws, standards, and frameworks (EU AI Act, South Korean AI Basic Law, U.S. state laws, NIST AI RMF, ISO 42001, GDPR), governing AI development (impact assessments, data lineage, model training/testing, bias detection, incident management), and governing AI deployment and use (risk evaluation, vendor assessment, ongoing monitoring, security testing, deactivation controls). The 2026 update reflects a critical shift from governing isolated AI models to governing complete AI systems, emphasizing that risks emerge from the interplay of infrastructure, data pipelines, human oversight, and model behavior — not from models alone.
AIUC-1
Compliant
AIUC-1 is the world's first certifiable standard specifically designed for enterprise AI agents, created by the Artificial Intelligence Underwriting Company (AIUC) in partnership with Stanford, Orrick, the Cloud Security Alliance, MIT, and MITRE. Unlike broader AI governance frameworks (ISO 42001, NIST AI RMF) that focus on management systems or voluntary guidance, AIUC-1 combines independent third-party auditing, 2,000+ technical evaluations, and quarterly adversarial testing to validate how AI agents actually behave in production — covering six core enterprise risk domains: security, safety, reliability, accountability, society, and data & privacy. Certification requires accredited auditors (Schellman is the first), remediation of material vulnerabilities, and continuous quarterly re-testing to maintain validity over a 12-month certificate period. The standard operationalizes EU AI Act, NIST AI RMF, ISO 42001, and MITRE ATLAS into concrete, testable controls focused on agent-specific risks — hallucinations, tool misuse, data leakage, prompt injection, and operational boundary violations — making it the primary trust signal for enterprises evaluating AI agent vendors. Organizations with existing governance can achieve certification in 5–10 weeks, and the standard updates quarterly to track evolving capabilities and threat landscapes.
SAFE AI / CoSET
Compliant
The AI Interpreting Solutions Evaluation Toolkit (August 2025) is a three-part, publicly available compliance framework released by the SAFE AI Task Force (Stakeholders Advocating for Fair and Ethical AI in Interpreting) in partnership with the Coalition for Sign Language Equity in Technology (CoSET), designed to help language access directors, compliance teams, CIO/CTOs, and procurement committees evaluate when and how to deploy AI and hybrid AI-human interpreting solutions — and when to reserve interactions for qualified human interpreters. Part A (Organization, Implementation, and Management) provides a risk-informed framework built around three decision pillars — organizational readiness, technical fitness, and total cost of adoption — operationalized through five practical checklists: organizational readiness across 8 domains (strategy, governance, infrastructure, privacy, training, QA, budget, rollout), setting-specific guidance for distinct environments (healthcare, legal, education, business), a risk factor assessment framework that scores use cases on a five-point severity scale with escalation triggers to human interpreters, vendor assessment across 10 evaluation categories (usability/accessibility, technical fitness, security/privacy, ethics, customization, support, escalation, compliance, cost, stability), and RFP guidance with template language including mandatory pilot and human-backup requirements. Parts B (Technical Specifications — UX/UI controls, model baselines, performance metrics) and C (Legalities & Practical Considerations — procurement criteria, policy language, day-to-day procedures) are forthcoming.
NIST AI RMF 1.0, AI 100-1
Compliant
The NIST AI Risk Management Framework (AI RMF 1.0, NIST AI 100-1), published January 2023, is a voluntary, rights-preserving, sector-agnostic framework from the National Institute of Standards and Technology that provides structured guidance for organizations designing, developing, deploying, or using AI systems to identify, assess, mitigate, and continuously monitor AI risks across the full system lifecycle. The framework is organized in two parts: Part 1 establishes AI as inherently socio-technical — where risks emerge from the interplay of models, data, people, and deployment context — and defines seven trustworthiness characteristics (valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed); Part 2 operationalizes these through four core functions — GOVERN (organizational policies, accountability structures, risk culture, legal/regulatory alignment), MAP (contextualizing and categorizing risks for specific AI systems including stakeholder impact analysis and use-case documentation), MEASURE (quantitative and qualitative assessment of identified risks including bias evaluation, performance testing, and TEVV), and MANAGE (risk treatment prioritization, response planning, post-deployment monitoring, appeal/override mechanisms, decommissioning, and continuous improvement) — each broken into detailed categories and subcategories that serve as implementation checklists. The framework has become the de facto AI governance vocabulary in the United States and aligns with ISO/IEC 42001, the EU AI Act, and OECD AI principles, with companion documents including the AI RMF Playbook, Generative AI Profile (NIST AI 600-1), and domain-specific use-case profiles for gap analysis between current and target risk postures.
OWASP Top 10 (2025)
Compliant
The OWASP Top 10 for Large Language Model Applications (v2025) is a community-driven, open-source security framework from the OWASP Foundation that identifies the ten most critical vulnerabilities in LLM-powered applications and provides prevention strategies, attack scenarios, and remediation guidance for each. The 2025 edition covers: LLM01 — Prompt Injection (direct and indirect manipulation of model inputs to bypass safeguards or trigger unauthorized actions), LLM02 — Sensitive Information Disclosure (unintended leakage of PII, credentials, or confidential data through model outputs), LLM03 — Supply Chain (compromised pre-trained models, poisoned datasets, or vulnerable third-party plugins), LLM04 — Data and Model Poisoning (tampered training data impairing security, accuracy, or ethical behavior), LLM05 — Improper Output Handling (insufficient validation and sanitization of LLM outputs enabling downstream code execution or injection), LLM06 — Excessive Agency (unchecked autonomy granted to LLM-based systems to take actions with unintended consequences), LLM07 — System Prompt Leakage (extraction of confidential system prompts exposing internal logic or security controls), LLM08 — Vector and Embedding Weaknesses (security risks in RAG retrieval pipelines and vector databases), LLM09 — Misinformation (hallucinated or inaccurate outputs compromising decision-making and trust), and LLM10 — Unbounded Consumption (resource exhaustion through excessive queries leading to denial of service or cost exploitation). The framework is designed for developers, security professionals, and architects building or deploying LLM applications, and maps each risk to related taxonomies including MITRE ATLAS and NIST AI RMF.
GDPR
Certified
AI certification
The General Data Protection Regulation, a comprehensive data protection law in the EU, governs how organizations must protect personal data and privacy.
PCI DSS
Certified
AI certification
Payment Card Industry Data Security Standard (PCI DSS) is a set of security standards designed to ensure that all companies that accept, process, store, or transmit credit card information maintain a secure environment.
ISO 27001
In progress
A globally recognized standard for establishing, implementing, maintaining, and continually improving an information security management system (ISMS).
HITRUST
In progress
