The rules are still being written, and the accountability for every automated decision your tools make is already yours. Aegis Ethos Consulting gives you what you need to adopt AI responsibly and prove it: enterprise-grade oversight for organizations of every kind, practitioner-led, framework-anchored, and right-sized to what you actually run. Its commitment runs from the communities enterprise consultants overlook to any organization that simply wants governance it can trust.
AEC's commitment is to the organizations most exposed to AI risk and least served by enterprise consultants, but the door is open to every sector and size. The frameworks are the same whether you're a five-person nonprofit or a regulated institution.
You're adopting AI tools to keep up, often on vendor contracts you didn't have the leverage to negotiate.
You serve the people most affected by algorithmic harm, and your mission depends on getting this right.
Public trust and statutory duty ride on every automated decision your office makes.
Four assessments, each answering a different question. They're built to work together: start fast, go deeper only where it's warranted, screen a specific tool for fairness, and test readiness before you adopt rather than after.
A focused, structured screen of your AI tools against the NIST AI Risk Management Framework (Govern, Map, Measure, Manage) to find where the governance gaps are. You get a written Findings Summary, a plain-language Risk Heat Map, and a clear next step.
An audit-depth engagement: real outcome data, stakeholder interviews, and vendor documentation, checked against the laws that govern automated decisions, including Title VII of the Civil Rights Act, the Federal Trade Commission Act, the Fair Housing Act, the Equal Credit Opportunity Act, and the European Union AI Act. A root-cause step traces each finding to its source. You get a Governance Risk Report with a remediation roadmap and clear flags for the findings you should take to your own counsel.
A structured evaluation of one specific AI tool (hiring software, tenant screening, credit scoring) against five federal and international standards, each finding mapped to the law it falls under. Calibrated for the smaller pools off-the-shelf tools weren't built for, and honest about when a sample is too small to trust.
The forward-looking counterpart to the audit work: not whether your AI use is defensible after the fact, but whether you're positioned to adopt AI well in the first place. It examines culture, capacity, governance design, vendor-contract exposure, and human oversight.
AEC's work rests on a set of convictions about responsible AI, drawn from risk practice, AI law and policy, and the communities most exposed to algorithmic harm.
Responsible governance isn't a compliance afterthought. It's the prerequisite for adopting AI at all.
AI carries the volume; people carry the judgment. A named human stays accountable for every AI-informed decision.
A risk lens finds the technical vulnerabilities; a rights lens finds the human ones. Serious work needs both.
The biggest risks come from who and what is absent from the data, not only from what's in it.
Fairness is tested with real methods, not taken on faith. An absence of alarms is proven, never assumed.
If a system can't explain why it decided something, it isn't ready to decide it. And safeguards mean something only when tied to real standards.
Every AEC instrument observes a firm boundary: it identifies findings that touch a legal standard for you to take to your own counsel. AEC does not provide legal representation, does not issue legal opinions, and does not determine whether any law has been violated.
Twenty-five years in regulated financial services taught me how to manage risk. My work now is helping organizations manage the risks of artificial intelligence, before those risks reach the people they affect.
I founded Aegis Ethos Consulting to do one thing well: assess how an organization uses AI, and tell them clearly and honestly where the risk actually sits. AI risk is rarely just a technical problem or just a legal one. It's a governance problem. Who is accountable? Is a human genuinely in the loop? Is anyone testing outcomes for fairness? Can the organization sustain responsible use over time? Those are the questions I help answer.
I believe AI should be built and used with human-centered ethics, transparency, and real governance. The human, not the machine, owns the thinking. If your organization is adopting AI and wants a clear-eyed, independent read on the risk, I'd welcome a conversation.