Secure AI Advisory

Deploy AI in your firm without surrendering what your clients trust you to keep private.

Secure AI deployment for family offices, law firms, private equity, and venture capital. Built by cybersecurity experts. We understand what is private, and how to keep it private.

19 minute read

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NSA / NSPD-54

Cyber Pedigree

F100 CISO

Practitioner Founders

4 Verticals

Family Office, Law, PE, VC

Confidential

By Design

The Problem

The Question Every Firm Like Yours Is Quietly Asking

Someone in your firm has already used ChatGPT this week. An associate dropped a deposition into it to summarize. A controller asked it to clean up a memorandum to the trustees. A deal-team analyst pasted a target's data room index into a public model and asked for a quick read on the carve-out. Whether you authorized it or not, the prompts are out there. The question on the next call from your principals, your managing partner, your LPs, or your insurance carrier will not be "are we using AI." It will be "do you know where it went." That is the question this page is built to help you answer.

Someone in your firm has already used AI without permission

Shadow AI is the rule, not the exception, across the four verticals we serve. Family office staff use it for correspondence around the principal's calendar. Associates use it for first-pass drafting. Investor relations teams use it to prep for LP calls. Operating partners use it to summarize portco board packs. The technology is genuinely useful, the productivity gains are real, and the people doing it are not bad actors. They are professionals trying to do their jobs faster on tools they assume are safe.

Most of those tools are not safe in the configuration the user is running. The free and Plus tiers of consumer LLMs retain prompts for thirty days and may train future models on the inputs. Browser extensions and "AI assistant" add-ons frequently route content through third-party servers without a credible audit posture. And the default settings of major productivity suites expose more data to AI features than most administrators realize.

What you actually need to know before AI becomes a problem

You do not need to ban AI. You need to know, for each data class your firm handles, what model is permitted, what license tier is required, what controls sit around it, and what record exists if a regulator, a client, an LP, or a court asks. That work has a name. It is secure AI deployment, and it is what we do.

We work with custodians of confidential information: family offices that hold a principal's privacy, law firms that hold a client's privilege, PE and venture firms that hold an LP's capital and a target or portfolio company's most sensitive material. Across all four, the underlying problem is the same: how do you let your team use AI to be productive without violating the trust that pays your bills.

Positioning

Why a Cybersecurity Firm, Not an AI Firm, Should Lead Your AI Deployment

We understand what is private, and how to keep it private. That sentence is the brand register, and it is also a thesis about who should be advising you on AI.

We understand what is private, and how to keep it private.

The people selling AI advisory today fall into three buckets. There are management consultants who recently added an AI practice and are still hiring the talent. There are AI-native consultancies that are excellent on prompt engineering and model evaluation but treat data handling as a downstream concern. And there are vendors selling a product wrapped in a deck. None of them have spent careers thinking about the way an adversary, a regulator, or an opposing counsel would treat a prompt log, a vector index, or a fine-tuning dataset.

We have. The founding partners of Trifident include a former NSA Chief of Operations, a co-author of National Security Presidential Directive 54, and Fortune 100 CISOs who have personally fielded the worst phone call a security leader can receive. AI deployment for our verticals is not a model-selection problem. It is a data-handling problem with a model attached. The question of which AI tool to deploy is downstream of the question your firm should already have written down: what data do we have, who owns the trust around it, and what controls survive an audit.

That is cybersecurity advisory work. We bring AI expertise to it; we do not let AI expertise replace it. The Big 4 will hand you a five-pillar framework. A vendor will hand you a license. A boutique AI shop will hand you a workshop. We hand you a written deployment plan, configured controls, and a quarterly operating relationship. The person who scopes your engagement is the person who does the work.

What We Secure

Six Categories of Concrete Work

When a Trifident engagement gets to the architecture phase, the work fans out across six categories. Each one is concrete. Each one has artifacts that survive a deposition, an SEC sweep, an LP DDQ, or an ABA ethics review.

01 / Data

Where prompts go, where they live, who reads them

Every prompt your team writes is a small data export. We map the path each prompt takes from the user's machine to the model and back: through which network, into which vendor's tenant, retained for how long, indexed against what, and visible to which third parties. We classify your data (trust documents, privileged matter files, MNPI, founder pitch decks, PII) and match each class to the AI tools that can lawfully and contractually receive it. The output is a written data-class to model matrix your team can actually follow.

02 / Identity

Who in your firm is allowed to use which models, against which data

AI access is identity work. Most leakage happens because the wrong person had the right permission, or the right person used a personal account on a corporate device. We use Microsoft Entra ID, Conditional Access, FIDO2, and identity-aware proxies to make sure the only people prompting against client material are credentialed, on a managed device, on a sanctioned tool. Where your firm uses Google Workspace, Okta, or another identity stack, we work with that.

03 / Vendor Risk

What your AI vendor's contract actually permits

AI vendor contracts are not yet standardized, and many of them quietly permit things your clients prohibit you from permitting. We read the DPA, the BAA, the OpenAI Enterprise Order Form, the Microsoft Customer Agreement, and the Anthropic Commercial Terms with the same eye we read an MSA in cyber due diligence. We tell you, in plain English, whether the contract you are about to sign is compatible with your client's outside counsel guidelines, your fund's LPA, your trust agreement, or the SEC posture you have committed to.

04 / Audit

A record that survives an LP DDQ, an SEC sweep, an ethics opinion

Every AI deployment we build produces an audit log: who used which model, against which data class, on which date, from which device, under which policy version. The log is queryable in plain English, retained per your records policy, and presentable to a regulator without an emergency vendor call. We have written this posture for firms preparing for the SEC AI sweep, ABA Formal Opinion 512 conformance, and ILPA-driven LP requests.

05 / Compliance

SEC, ABA, GLBA, state privacy law, contractual NDAs

We map the AI deployment to the regimes that already govern your firm. SEC Rules 38a-2 and 206(4)-9 for advisers. ABA Model Rules 1.1, 1.6, 1.4, and 5.3, as interpreted in Formal Opinion 512. The Gramm-Leach-Bliley Safeguards Rule for any firm holding customer financial information. State privacy laws (CCPA, NYDFS, the Texas TDPSA, the patchwork). And the contractual web your firm has already signed: outside counsel guidelines, side letters, trust agreements, NDAs with portfolio companies. The AI architecture is built to pass each one.

06 / Operational

The day-to-day controls your team actually follows

A control your team will not follow is not a control. We write the policy in language your associates and analysts will read. We build the approved-tools list and keep it current as vendors change terms. We deliver training calibrated to the verticals we serve. And we set up the channels (Slack, Teams, email, a partner-on-call) for the moments when a team member is unsure whether a prompt is safe and needs an answer in five minutes, not five days.

By Vertical

Tailored to Your Firm's Specific Privacy Reality

The four verticals we serve share a buyer-side problem. They diverge sharply on the language of risk, the regulator, and the consequence of getting it wrong. The next four cards are the only place on this page where we treat each vertical individually. Everything else is cross-vertical because the underlying methodology is.

Family Office

Principal Privacy and Trust Document Confidentiality

A family office holds the most personal version of confidential information our firm encounters. Trust documents, beneficiary lists, the principal's medical letters, family travel calendars, the next-generation succession memo, the prenup. Your staff is small and your tooling is heterogeneous in family offices like yours, and your principal asked a week ago whether the family could use ChatGPT to summarize the next Trustees' meeting. We help family offices answer that question with a written deployment plan covering tenant configuration, principal-grade identity controls, vendor contracts your outside counsel will sign off on, and a private model option for the documents that should never reach a public API.

Law Firm

Privilege, Ethics, and Outside Counsel Guidelines

Your matter team holds a client's privilege at law firms like yours, and ABA Formal Opinion 512 has made the duty of competence on AI an ethics issue, not an IT issue. You are also seeing a wave of outside counsel guidelines from banking, life sciences, and PE clients that prohibit public LLMs and require an audit log. We help law firms reach a defensible posture on three fronts: an AI policy and approved-tools list that conforms to Model Rules 1.1, 1.6, 1.4, and 5.3; a tenant configuration of Microsoft 365 Copilot or its alternatives that respects matter walls and information barriers; and the audit artifacts a malpractice carrier or opposing counsel will eventually ask to see.

Private Equity

MNPI, LP Data, and Portfolio Operations

Your firm holds two kinds of confidential information at once at private equity firms like yours: the LP's capital and the target or portco's MNPI. The SEC has adopted Rules 38a-2 and 206(4)-9 on cybersecurity, the AI sweep has begun, and ILPA's DDQ now includes AI governance questions that did not exist eighteen months ago. We help PE firms write the AI policy at the GP level, deploy approved tools the deal team will actually use, build audit logging that survives an SEC examination, and extend the framework into the portfolio companies.

Venture Capital

Founder Confidentiality and Deal-Flow Integrity

Your deal team lives in AI tools at venture capital firms like yours because memo writing, market mapping, and competitive analysis are exactly the work LLMs are best at. Your portfolio companies' pitch decks, term sheets, cap tables, and competitive intelligence sit inside those threads. A leak embarrasses you with the founder, costs you a follow-on, and damages the deal flow that keeps the lights on. We help venture firms put the deal team's AI usage on a sanctioned footing without slowing them down: approved tools, identity-bound access, a clear rule for what stays out of any public model, and an LP-facing answer for the moment a fund DDQ asks how your firm governs AI.

Where does your firm actually stand on AI deployment?

Take the twelve-section discovery questionnaire. It takes 60 to 90 minutes, walks your team through the same diagnostic we use on every engagement, and leaves you with a clearer picture of your AI posture whether you hire us or not.

Start the Discovery Questionnaire

Engagement Model

How a Trifident Secure-AI Engagement Works

We work in four phases. The phases are sequential by design, but the second and third overlap once the architecture is agreed, and the fourth is open-ended.

Phase One

Discovery

Begins with the AI Discovery Questionnaire. Twelve sections, around ninety questions, covering engagement context, current state, your Microsoft 365 or Google Workspace footprint, data inventory, identity, security tooling, compliance regime, key workflows, AI use cases, risk tolerance and budget, vendor preferences, and timeline. Every question has a "Why we ask" expansion. After submission, a founding partner spends sixty minutes with your team reviewing the responses, surfacing the questions the questionnaire could not, and confirming the scope.

Phase Two

Architecture

A written deployment plan tailored to your firm. Tenant configuration: license tier, Conditional Access, Purview labels and DLP, Defender for Cloud Apps, audit retention. Data classification: every data class your firm handles mapped to the AI tools it can lawfully reach. Identity and access: who prompts which models, on which devices, against which data. Vendor selection: a recommended stack with the contract terms vetted. Audit posture: the log model and the regulator-facing answer. Most architectures are delivered four to six weeks after discovery completes.

Phase Three

Implementation

We work alongside your IT team or your MSP to configure the controls, write the policies, and validate the deployment. We prefer to work with the team you already have rather than insert a layer of consulting between them and the work. We write the AI acceptable-use policy, build the approved-tools list, and deliver training calibrated to your audience: a partner audience reads differently from an associate audience reads differently from family office staff. Firms of ten to fifty people typically reach a deployed and validated state in six to ten weeks.

Phase Four

Operation

An AI deployment is not a one-time project. Vendor terms change, models are upgraded, regulators publish new guidance, and your team's use cases mature. We move to a quarterly cadence: a working session every ninety days to review the audit log, surface vendor changes, retest the controls, and update the approved-tools list. Active incident support is on retainer. When the SEC publishes the next sweep priorities or the ABA issues the next opinion, we tell you how your posture compares.

Who You Are Hiring

Nation-State Caliber Cybersecurity Advisory

Trifident is nation-state caliber cybersecurity advisory. The founding partners include a former Chief of Operations of NSA's defensive cybersecurity organization and Division Chief of NSA's offensive cyber capabilities; a co-author of National Security Presidential Directive 54 (CNCI); three Fortune 100 CISOs who have personally led incident response at scale; and a CMMC Registered Practitioner who contributed to the development of federal cybersecurity standards. The full leadership profile lives at our leadership page.

The same person who scopes your engagement does the work.

There is no leverage model, no rotating cast of associates, no learning curve subsidized by your budget.

We do not publish client lists or name engagements. The pattern of work, in audience archetypes, looks like this. A Midwest family office introducing Microsoft 365 Copilot to a six-person staff that supports a principal with multi-generational holdings. A 70-attorney boutique litigation firm writing its first AI policy in response to updated outside counsel guidelines from two banking clients. A $1.2 billion AUM mid-market PE fund preparing the AI section of its next ILPA-aligned LP DDQ ahead of a Fund III close. A $400 million early-stage venture firm bringing its deal team's ChatGPT and Claude usage onto a sanctioned, identity-bound footing. The shape of the work is consistent. The data is yours.

The first conversation is confidential, and there is no obligation.

We tell you within thirty minutes whether we are the right firm for your situation. If we are not, we say so.

Start the Discovery Questionnaire

FAQ

Common Questions About Secure AI Deployment

Fifteen questions, drawn from the conversations we actually have with family offices, law firms, PE, and VC.

Can my firm use ChatGPT with confidential client documents?

It depends on which ChatGPT, configured how. The free, Plus, and Pro tiers retain prompts for up to thirty days and may train future models on your inputs unless you opt out. ChatGPT Enterprise, Team, and Edu do not train on your data and offer SOC 2 Type 2 attestation along with a Zero Data Retention (ZDR) option for API workloads. Whether those tiers are appropriate for privileged matter, MNPI, or trust documents is a separate question of vendor risk, contractual posture, and your firm's data classification, which is what we work through in discovery.

Is Microsoft Copilot safe for a 60-attorney firm?

Microsoft 365 Copilot inherits the data boundary of your Microsoft 365 tenant, which is a strong starting point. Whether it is safe in your firm depends on tenant configuration (E5 versus E3, Microsoft Information Protection labels, Conditional Access, audit retention), license tier, the terms of your DPA, and what you let Copilot index. With the right configuration it can be appropriate for matter work; with the default configuration it can leak across information barriers. We help firms reach the right configuration before deployment, not after a near-miss.

What's the difference between a ZDR and a BAA?

A Zero Data Retention agreement (ZDR), in the AI context, is a contractual commitment from the vendor that your prompts and completions are not retained beyond the immediate inference call and are not used to train future models. A Business Associate Agreement (BAA) is a HIPAA-required contract between a covered entity and a service provider that handles Protected Health Information. They serve different purposes: ZDR governs persistence and training; BAA governs HIPAA liability allocation. Many AI vendors offer one and not the other, and which you need depends on the data classes your firm actually handles.

Can my private equity firm use AI on data-room MNPI?

The honest answer: only with a deployment architecture designed for it. Public consumer LLMs are not appropriate for MNPI under any normal configuration. An enterprise-tier API with ZDR and a DPA that prohibits training and persistence can be appropriate for some MNPI workflows. A private LLM deployment, where the model and prompts both stay on infrastructure you control, is appropriate for the most sensitive deal-stage MNPI on a public-company target. The harder question is auditability: can you prove to the SEC and your LPs that the MNPI did not leak. That audit posture is what we build.

Does Microsoft 365 Copilot use my data to train models?

Microsoft has stated, contractually, that Microsoft 365 Copilot prompts and responses are not used to train the underlying foundation models, that customer data stays inside the Microsoft 365 service boundary, and that grounding content is fetched at query time. The protection depends on which Copilot product (the consumer version is different), which license tier, and which DPA you have signed. For regulated firms, we pair Copilot with Purview labels and Conditional Access rather than rely on the contract alone.

What is ABA Formal Opinion 512 and how does it affect our AI use?

Formal Opinion 512, issued by the ABA Standing Committee on Ethics and Professional Responsibility in July 2024, addresses generative AI under the Model Rules. In short: lawyers have a duty of competence on the AI tools they use (Rule 1.1), must protect client confidentiality including in prompt content (Rule 1.6), must communicate with clients about AI use that materially affects representation (Rule 1.4), and must supervise AI tools as they would supervise non-lawyer staff (Rule 5.3). Most firms now need a written AI policy, a client-disclosure approach, and a vetted approved-tools list to credibly comply.

Should LP DDQs include AI governance questions?

Increasingly, yes. ILPA's diligence template and many large LPs added AI governance questions to standard DDQs and side-letter requests through 2025. Common asks include a written AI policy, an approved-tools list, an acceptable-use program, vendor risk process for AI vendors, audit log retention, and oversight at the portfolio company level. A well-prepared GP has the artifacts ready before the question is asked.

How do we prevent associates and analysts from leaking client data into ChatGPT?

Three layers, in order of effectiveness. First, identity and DLP: Microsoft Defender for Cloud Apps, Purview Endpoint DLP, or a dedicated AI gateway can block uploads to unsanctioned LLMs at the network or endpoint layer. Second, an approved alternative: when you give the team a sanctioned tool that genuinely works for their job, unauthorized usage drops sharply. Third, policy and training. Tools without policy, or policy without tools, both fail.

What is a "private LLM deployment" and when do we need one?

A private LLM deployment is an instance of a language model that runs in infrastructure you control, with no shared inference and no data leaving your environment. Practically this looks like running an open-weight model (Llama, Mistral, Qwen) on a server in your office, your data center, or a single-tenant cloud, sometimes inside a Confidential Computing Trusted Execution Environment (TEE) for an additional hardware-level boundary. You need one when your data has properties no third-party SaaS posture can satisfy: trust documents at the highest sensitivity, attorney-client privilege on bet-the-firm matters, deal-stage MNPI on public-company targets, or contractual prohibitions on third-party processors. Most firms do not need a private deployment for everything, only for one or two specific data classes.

Should we run AI on-prem or in Azure?

Both can be appropriate. On-prem (your data center, your office, a single-tenant rack) gives the strongest answer to "where did this prompt go" and is the only option for some contractual postures. Azure, AWS, or Google Cloud with a private deployment, customer-managed encryption keys (BYOK), and a tenant configuration that pins data residency can match nearly the same posture with less operational burden. The choice depends on your existing infrastructure, your team's operational maturity, the data classes you intend to run, and your auditor's view.

How do you audit what an LLM saw?

Every AI deployment we build produces an immutable audit log: which user (by identity, not by IP), which model, against which data class, on which date, from which device, under which policy version. For Microsoft 365 Copilot, that log lives in the Purview audit pipeline. For OpenAI Enterprise and Anthropic Commercial, it lives in the vendor's audit endpoint, and we configure ingestion into your SIEM. For private deployments, we instrument the inference layer directly.

How long does a Trifident secure-AI engagement take?

Discovery, including the questionnaire and a working session with a partner, takes two to three weeks. The architecture phase, which produces the written deployment plan, takes four to six weeks. Implementation timing scales with the size of your environment; firms of ten to fifty people typically reach a deployed and validated state in six to ten weeks. We then move to a quarterly operating cadence.

Do we have to abandon the AI tools our team is already using?

Almost never. The first step in discovery is inventorying what is already in use. The goal is to bring productive shadow-AI usage into a sanctioned posture, not to ban it. Sometimes the answer is your team is already using the right tool, but configured wrong. Sometimes it is this tool is fine for low-sensitivity work but not for matter files, MNPI, or principal-grade material. We come out of discovery with a tool-by-tool, data-class-by-data-class verdict that reflects how your firm actually works.

How is this different from hiring a Big 4 firm to advise on AI?

Three differences. We are cybersecurity advisors first, not management consultants who recently added an AI practice. The four verticals on this page are our practice, not a horizontal we are testing. And the deliverable is a working deployment plan executed alongside your IT team, not a slide deck. The person who scopes your engagement is the person who does the work.

Is the initial briefing actually confidential?

Yes. We do not publish client lists, do not name engagements in marketing, and do not require an NDA before a first call, although several clients engage us through their counsel for additional privilege protection. Anything you share in the briefing is held to the same confidentiality standard as a paid engagement. JMAP, S/MIME, signal-grade voice, or a courier are available if your situation calls for them.

Begin

Begin a Confidential Conversation

Two ways to start; they lead to the same place. Both are confidential, and there is no obligation.

If you want a clearer picture first

Take the Discovery Questionnaire. It takes 60 to 90 minutes. You will leave it with a more honest internal answer than most firms have today, whether you hire us or not.

Start the Discovery Questionnaire

If you want to talk through a specific situation

Schedule a confidential briefing with a founding partner. Thirty minutes. We will tell you within that thirty whether we are the right firm for what you are trying to do. If we are not, we will say so.

You can also reach us directly at [email protected]. Founding partners read the inbox.

Discovery Questionnaire