- Mon - Fri: 8:00AM - 5:00PM
- info@arcisphere.com
- 215-378-8155
Home – AI Regulation & Compliance
Protect your organization from regulatory penalties, algorithmic bias lawsuits, and security breaches that AI systems create. We implement NIST AI Risk Management Framework and comprehensive governance ensuring your AI deployments satisfy regulators while driving business value.
Your competitors are deploying AI systems without understanding the regulatory exposure they’re creating.
Healthcare organizations implement AI that accidentally exposes patient data, triggering HIPAA violations and multi-million dollar penalties. Financial institutions deploy algorithms that create discriminatory lending patterns, resulting in lawsuits that destroy shareholder value. Manufacturing companies use AI for safety-critical decisions without governance frameworks, exposing them to catastrophic liability.
AI governance isn’t about slowing innovation. It’s about implementing AI systems that deliver competitive advantage without creating the incidents that make headlines and end careers.
Have questions, need guidance, or want to explore how we can support your goals — our team is here, ready to help. Let’s move forward together.
We implement the NIST AI Risk Management Framework—the federal standard for identifying, assessing, and mitigating AI-specific risks that traditional security programs don't address. Complete risk assessment, governance framework aligned with NIST standards, risk mitigation strategies, and documentation satisfying auditors. Timeline: 8-12 weeks for single systems, 3-6 months for enterprise programs.
ISO 27000 provides the information security management framework. We extend it to address AI-specific security challenges: training data security, model deployment vulnerabilities, AI-generated output risks, access controls, and incident response procedures for AI failures.
We develop comprehensive governance frameworks tailored to your organization: AI use policies, risk assessment procedures, monitoring and audit requirements, incident response protocols, and training materials ensuring teams understand governance requirements.
AI systems evolve. Governance requires continuous monitoring: regular audits verifying compliance, model performance monitoring detecting bias or drift, regulatory tracking, incident investigation, and quarterly compliance reporting for leadership.
We identify every AI system in your organization—including shadow AI. For each system, we assess what decisions it makes, what data it uses, what regulations apply, and what risks exist if it fails.
We implement governance frameworks proportional to each system's risk level, aligned with NIST AI RMF, ISO standards, and your industry's specific regulations.
We document clear policies and train your teams—developers, business users, compliance officers—ensuring everyone understands their responsibilities.
We establish monitoring systems tracking AI performance, detecting compliance drift, and flagging incidents requiring investigation.
You need governance frameworks proving to auditors and boards that AI deployments are controlled and compliant.
You're accountable when AI systems violate regulations. You need visibility into AI deployments and assurance they satisfy requirements.
You need frameworks quantifying AI risks and demonstrating appropriate mitigation.
You need honest assessment of where your organization is vulnerable and what it costs to address it.
HIPAA compliance for AI using patient data. FDA guidance for AI in medical devices. Bias prevention in diagnostic systems.
Fair lending requirements for AI credit decisions. SEC guidance for AI trading systems. AML/KYC compliance.
Discrimination prevention in AI underwriting. Claims processing compliance. Regulatory reporting.
Safety-critical AI governance. Export control compliance. Federal contract requirements.
Safety system governance. Product liability considerations. Quality management integration.
Our founder spent 20+ years at IBM implementing enterprise systems where compliance wasn't optional. We provide frameworks your organization can actually implement.
We hold official certifications in NIST AI Risk Management Framework implementation—validation that we implement AI governance according to federal standards.
We focus on governance gaps that create incidents: algorithmic bias, training data containing regulated information, and monitoring blind spots that hide problems until regulators discover them.
Schedule a consultation to discuss your AI governance needs. We’ll provide honest assessment of where you’re exposed and realistic options for addressing it.
215-378-8155
info@arcisphere.com
IT security protects systems from external threats. AI governance addresses different risks: algorithms making biased decisions that violate civil rights laws, AI outputs exposing confidential information, and AI making safety-critical decisions without appropriate oversight. These require specialized frameworks like NIST AI RMF.
Yes. Commercial AI tools create risks: employees putting confidential data into systems that train on user inputs, AI-generated content violating copyright, and AI making decisions without human oversight where regulations require it.
Best case: nothing, and you got lucky. Realistic case: you discover governance gaps during an audit and face rushed, expensive remediation. Worst case: an AI system creates a regulatory violation, discrimination lawsuit, or security breach resulting in penalties and career consequences.
For a single AI system: 8-12 weeks including risk assessment, governance framework development, policy documentation, monitoring setup, and team training. For enterprise-wide programs: 3-6 months for initial implementation.
Initial implementation requires time, but proper governance prevents incidents that truly slow AI programs: discovering compliance gaps mid-deployment, regulatory investigations that freeze development, and bias scandals that create executive fear of all AI initiatives.
Jim Sullivan founded Arcisphere Technologies after 20+ years in enterprise software engineering, including extensive experience at IBM implementing mission-critical systems for Fortune 500 clients where regulatory compliance and operational reliability were non-negotiable.
He holds certifications in NIST AI Risk Management Framework implementation, ISO 27000 information security management, and Kubernetes infrastructure. Jim’s experience spans the complete lifecycle of enterprise technology—from initial architecture through production deployment and ongoing governance—with particular focus on systems where failures have severe regulatory, financial, or reputational consequences.
His approach to AI governance reflects years of experience with enterprise realities: governance frameworks must be comprehensive enough to prevent incidents while practical enough that organizations can actually implement them.
