Why Public Companies Need Private and Custom LLMs for Compliance

If you run a public company, you already know the regulatory environment is a minefield. Every word in an earnings call, every risk factor in a 10-K, and every decision in a board meeting can move markets.

Now layer in AI.

The appeal of large language models (LLMs) is obvious: they can draft documents, analyze thousands of pages in seconds, and even surface patterns in filings and contracts that humans would miss.

But there’s a catch: public companies can’t afford compliance missteps.

Using a consumer-grade AI tool is like drafting your earnings guidance on a sticky note and leaving it in a coffee shop.

The risks are too high.

That’s why private and custom LLMs are quickly moving from “nice-to-have” to non-negotiable for public companies.

1. Material Non-Public Information (MNPI) & Insider Trading Risks

The SEC doesn’t play around with MNPI. Remember when an intern accidentally leaked Apple’s earnings numbers early? The stock moved billions of dollars in minutes. That’s how sensitive information is before official disclosure.

• Risk with public LLMs: Feeding a draft earnings release, an acquisition plan, or even internal forecasts into a public AI tool could be considered an unauthorized disclosure. Once the data is outside corporate firewalls, you’ve lost control.
• Private LLM benefit: Keep all analysis in-house. A private model can draft, summarize, and scenario-test earnings scripts without risking a Reg FD violation.

Anecdote: One CFO we spoke to said his team uses a private LLM to stress-test earnings call Q&A. They input “what tough questions might analysts ask about our margins?” The model generates a list that helps leadership prepare—without worrying that sensitive numbers might leak into the wild.

2. Shareholder & Customer Data Privacy

Public companies are custodians of enormous amounts of personal data: shareholder registries, employee payroll, customer information. Think of Equifax’s breach in 2017—it cost them over $700 million in fines and settlements.

• Risk: Sending PII into a public LLM may violate GDPR, CCPA, HIPAA, or financial privacy laws.
• Private LLM benefit: The model runs behind corporate firewalls, ensuring all personally identifiable information (PII) is encrypted and compliant.

Example: A retail company with millions of loyalty program members used a private LLM to scan call-center transcripts for recurring complaints. Instead of exposing sensitive customer info to an external AI, they trained their own model with their data secure on their own vector database. The insights cut customer churn by 8%.

3. Sarbanes-Oxley (SOX) Record Retention & Audit Trails

SOX requires strict control over financial reporting systems. Every adjustment, every memo tied to financial results must be logged.

• Risk: Public LLMs don’t provide audit trails. If a financial draft gets “improved” by an external AI, how do you prove compliance during an audit?
• Private LLM benefit: Built-in logging and document retention aligned with SOX controls. Every query and output is stored, searchable, and defensible.

Anecdote: An auditor once asked a Fortune 500 finance team to show the rationale behind a late adjustment. Without clear documentation, they could’ve faced penalties. A private LLM can tag, store, and explain why specific language was used in filings.

4. SEC Disclosure Rules (10-Ks, 10-Qs, Proxy Statements)

Draft filings are among the most sensitive documents in any public company. Premature disclosure could cause lawsuits and market chaos.

• Risk: A public LLM might store or learn from filings before they’re public, essentially becoming an “insider.”
• Private LLM benefit: Enables secure drafting and cross-referencing. A company can ensure its 10-Q matches its 10-K risk language without leaks.

Example: A biotech firm used a custom LLM to check consistency between their SEC filings and press releases. The model flagged where “clinical success rates” were described differently—preventing a possible shareholder lawsuit for inconsistent disclosures.

5. Board Governance & Fiduciary Duties

Boardrooms are where the most sensitive discussions happen: succession planning, executive compensation, strategy pivots.

• Risk: Exposing board decks or meeting transcripts to public models could breach confidentiality agreements.
• Private LLM benefit: Allows secure summarization of board minutes, cross-comparison of prior resolutions, and instant Q&A for directors.

Anecdote: One Fortune 100 board implemented a private LLM to prepare digestible summaries of 300-page pre-read packets. Directors said it reduced prep time by half, while legal counsel stayed comfortable knowing nothing left the firewall.

6. M&A Transactions and Deal Confidentiality

M&A deals are famously secretive. Leaks can derail negotiations or trigger regulatory headaches.

• Risk: Feeding diligence documents or draft merger agreements into a public LLM could breach NDAs or antitrust review protocols.
• Private LLM benefit: Secure “deal room” analysis where legal, finance, and strategy teams can query thousands of pages of diligence material without exposure.

Example: During a $5B acquisition, a private equity firm used a custom LLM to sift through thousands of contracts. It flagged change-of-control clauses in minutes—work that would’ve taken junior associates weeks.

7. Litigation Hold & eDiscovery Requirements

When litigation hits, companies must preserve all relevant communications. Using uncontrolled AI could destroy or alter discoverable data.

• Risk: Loss of compliance with litigation hold orders.
• Private LLM benefit: Integrates with eDiscovery platforms, surfacing relevant case law or documents while preserving chain of custody.

Anecdote: In one class-action case, legal teams spent months combing through emails. A private LLM can instantly cluster documents, flag themes, and rank relevance—without breaking compliance rules.

8. Cybersecurity & SEC’s New Disclosure Rules

The SEC now requires disclosure of material cybersecurity incidents. But what if the reporting process itself introduces a vulnerability?

• Risk: Sharing incident details with a public LLM could create a second breach.
• Private LLM benefit: Securely analyzes incident reports, drafts disclosures, and provides impact assessments—all within controlled infrastructure.

Example: After a ransomware attack, one Fortune 500 company used a private LLM to summarize forensic reports for the board and draft the 8-K disclosure. They did it in days, not weeks—without risking sensitive data on external servers.

Conclusion

Public companies have more to lose than anyone else when it comes to data leakage, premature disclosure, or compliance missteps.

• Open/public AI tools = risky shortcuts.
• Private/custom LLMs = compliant, auditable, and secure productivity engines.

At LLM.co, we help enterprises deploy AI responsibly—aligned with SEC rules, SOX requirements, privacy laws, and board-level governance. Because for public companies, “move fast and break things” doesn’t cut it. The real mandate is: move smart and stay compliant.

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