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Agentic 10-K

PATRIOT NATIONAL BANCORP INCPNBK

AI agent disclosures in SEC filings

As of 2026-03-31, PATRIOT NATIONAL BANCORP INC (PNBK) disclosed AI-agent language in 1 SEC filing since 2026-03-31 (source: Agentic 10-K, from SEC EDGAR).

Market cap
$107MYahoo Finance, 2026-10-21
First seen
2026-03-31
Latest filing
2026-03-31

Matching filings by quarter

012Q1 '25Q2Q3Q41Q1 '26Q2Q3Q4*
Filings* Quarter in progress

What the filings cover

Functions named

Legal & compliance

Vendors and products named

None named

Source

1 filing since 2025-01-01. All EDGAR filings (opens in a new tab)

Stage and function are keyword-rule labels, not the company’s words.

In its own words

Sentences copied from the filings, whitespace normalised.

“AI models, particularly generative or agentic AI models, may produce outputs or take action that is incorrect, that reflects biases included in the data on which they are trained, that results in the release of private, confidential, or proprietary information, that infringes on the intellectual property rights of others, or that is otherwise harmful.”

10-KFiled 2026-03-31Source on sec.gov (opens in a new tab)Filing index (opens in a new tab)

All matching filings

10-K filed 2026-03-31 Filing index (opens in a new tab)

10-KFiled 2026-03-31

PATRIOT warns that generative or agentic AI models may produce incorrect, biased, or harmful outputs and expose it to liability.

“AI models, particularly generative or agentic AI models, may produce outputs or take action that is incorrect, that reflects biases included in the data on which they are trained, that results in the release of private, confidential, or proprietary information, that infringes on the intellectual property rights of others, or that is otherwise harmful. Further, we may rely on AI models developed by third parties, and, to that extent, would be dependent in part on the manner in which those third parties develop and train their models, including risks arising from the inclusion of any unauthorized material in the training data for their models and the effectiveness of the steps these third parties have taken to limit the risks associated with output of their models, matters over which we may have limited visibility. Any of these risks could expose us to liability or adverse legal or regulatory consequences and harm our reputation and the public perception of our business or the effectiveness of our security measures.”