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The following sections draw exclusively from materials already filed by Plaintiff—specifically ECF Nos. 14-38, 14-41, 286-13, and 286-14—each submitted by Mphasis to this Court as public exhibits. At the time of filing, none of these documents were sealed, redacted, or marked as confidential. Only after Defendant cited them in his defense did Plaintiff attempt to retroactively relabel them as “trade secrets,” in a transparent effort to suppress unfavorable but lawfully disclosed facts. The evidentiary record speaks for itself. All commentary herein is based solely on Mphasis’s own public docket submissions and is protected by law.

14-38 ocr p1-46 (pdf)Download
14-38 ocr p47-94 (pdf)Download
14-41 ocr p1-63 (pdf)Download
14-41 ocr p64-113 (pdf)Download
286-13 (pdf)Download
286-14 (pdf)Download

Defendant Key Contributions

Inside QBE’s 2024 Breach: What Really Happened — A Whistleblower’s Firsthand Account

As a former technical contributor to QBE’s infrastructure modernization and document intelligence efforts, I witnessed firsthand how endpoint mismanagement, policy conflicts, and ignored internal warnings led to one of the most avoidable healthcare data breaches in recent memory.

Key Contributions

  • Optimization Demo (Legal NDA Platform): Documented fixes to a broken NDA document processing pipeline. Demonstrated in this live technical demo.
  • Legal NDA Logic Remediation: Contributions were acknowledged internally and referenced in Jira tickets long after my departure.

1. Platform Optimization & Demo (Legal NDA Remediation)

As a domain expert, Defendant developed and demonstrated a technical remediation of the QBE Legal NDA platform—originally architected by Accenture—which suffered from material execution failures. These deficiencies were captured in detail via forensic exhibits, including 4Dillip.xlsx, and substantiated through live-screen video recordings.

Exhibit Video

Despite Defendant’s termination, the Accenture team at QBE continued utilizing Defendant’s documented methodologies to triage and repair critical system defects—affirming the value and originality of Defendant’s contribution.

2. Enhanced Legal NDA Document Decoding for QBE

Defendant materially improved the Legal NDA decoding pipeline—shifting error-prone record retrievals into deterministic, auditable outcomes. The failure patterns ("data not found") highlighted in 4Dillip3.xlsx triggered follow-on Jira tickets by QBE staff, reflecting continuing reliance on Defendant’s diagnostic work and subject-matter expertise.

Acknowledged in Internal Communications:

  • Defendant:
     “Just checking in from London… if anyone has questions on the '4dilip.xlsx' spreadsheet highlighting the 'data not found' prompt returns… reach out as always.”  
  • Mphasis employee (acting QBE Project Manager):
     “Yes, we discussed the issue the last two days. Two new Jira tickets have been raised—one assigned to Manjusha, the other to Ishita when she returns.”

Disclaimer: Protected Legal Submission

This site contains content directly related to the pending matter Mphasis Corporation v. Defendant, Case No. 25-cv-3175 (JMF), before the United States District Court for the Southern District of New York.

All materials are presented in furtherance of a constitutionally protected and statutorily authorized whistleblower defense. They include sworn declarations, factual assertions, legal arguments, and evidence submitted by the undersigned, appearing pro se.

These disclosures are expressly protected under:

  • 18 U.S.C. § 1833(b) – Defend Trade Secrets Act (Whistleblower Immunity)
  • 18 U.S.C. § 1514A – Sarbanes-Oxley Act (SOX)
  • 15 U.S.C. § 78u-6(h) – Dodd-Frank Act (SEC Whistleblower)
  • N.Y. Lab. Law § 740 – New York Whistleblower Protection Law

No information herein was obtained through unauthorized access or improper means. All content is submitted in good faith to expose and document systemic compliance failures and risks to public interest, consistent with applicable law.

Selected Achievement

He enhanced document decoding for QBE by integrating an LLM with a linguistic model, reducing processing time from 35s to <2s without compromising accuracy.

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DISCLAIMER: This summary is a factual representation of public filings and protected disclosures made by the Defendant in accordance with rights under 18 U.S.C. § 1833(b), Sarbanes-Oxley, Dodd-Frank, and New York Labor Law § 740. No confidential, sealed, or privileged information has been disclosed beyond what has been publicly filed in the U.S. District Court for the Southern District of New York. This document is provided for transparency and does not constitute legal advice or waiver of any legal rights.

  • Brad Kelly PI
  • Mphasis Retaliation & Age
  • Dual-Use Endpoint
  • Forgotten QBE Laptop
  • Defendant Domain Expert