Secure code scanning for AWS bedrock code
project
fecaa31f94d7a4a0
Normalized: secure code scanning for aws bedrock code
moreland_contracts
Entity Properties (gold project table)
project_id
fecaa31f94d7a4a0
name
Secure code scanning for AWS bedrock code
client_id
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canonical_metadata
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created_at
2026-05-02 02:29:59.020102+00:00
updated_at
2026-05-02 02:29:59.020102+00:00
status
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billable
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recurring
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squad
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qa_partner
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project_manager
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lead_dev
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description
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contract_total_fee
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53%
Separation Confidence
How distinct this entity is from others. Higher means no close matches existed when it was created. Lower means a near-match was rejected just below the 80% threshold — worth reviewing.
Moderate — a somewhat similar entity exists
100%
Avg Match Confidence
The average confidence score across all active source mappings. Shows overall quality of linkage between source records and this canonical entity.
Strong source linkage
Source Mappings (1)
| Source | Source ID | Display Name | Confidence | Method | Status | Actions | |
|---|---|---|---|---|---|---|---|
| moreland_contracts | d16280b83b5a7d242e207ee4cb1de1ea | Secure code scanning for AWS bedrock code | 1.00 | fuzzy+embedding | Active |
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Source Record
bronze_moreland_contracts_ma_project_contracts
file_hash
d16280b83b5a7d242e207ee4cb1de1ea
file_name
AI use cases.docx
file_path
https://morelandpartners.sharepoint.com/sites/MorelandConnect-BusinessDevelopment/_layouts/15/Doc.aspx?sourcedoc=%7B9C6EDED8-36A4-40B5-84F4-E462C0554838%7D&file=AI%20use%20cases.docx&action=default&mobileredirect=true
company
Info Security
project_name
Secure code scanning for AWS bedrock code
contact_person
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contract_type
time_and_materials
hourly_rate
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estimated_hours
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total_fee_estimate
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brief_project_description
Develop workflows for LLM based QA and comprehensive scans of AI generated code against security requirements.
start_date
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extracted_at
2026-07-01T02:20:49.592307+00:00
raw_text_preview
Info Security
**(Access issue). Secure code scanning for AWS bedrock code (ie. AI generated code), Input/Output Validation (What is goal? Why do it here instead of #2. Will slow down Bedrock.)
Workflow for LLM based QA to check whether the code/output is meeting all the project’s criteria/security requirements in the input/prompts
Workflow for a more comprehensive LLM based scan of the same output /generated code but against a broader and specialized security requirements set along, ideally using a tuned and more stable/predictable Bedrock LLM
**(POC 1 App Framework or GitHub actions) 1 week POC little short (leverage existing library) Develop library of LLM friendly security requirements that includes framework and vuln specific requirements (ie. Secure secret handling in headless apps, input validation for Java or Spring based code)
Integrated into “repo” pipeline/repo/bedrock workflows so it’s developer friendly and automatically invoked
(Large scale difficult before Jan 31) Leverage LLM for data analysis, such as
summarizing existing application security vulnerabilities and findings (across multiple tools)
correlating assets in asset inventory, with IP addresess in tickets, or public information
AWS Macie for summarizing DLP events (ie. text file in S3 bucket)
* stretch Develop framework (1 week POC) CodeRabbit to periodically test how well LLMs detect or describe vulnerabilities (ie. Evaluate (long tail) AI Models for security effectiveness, could be against specialized security tools)
Privacy
**(Access Issue) Develop personal data filter (e.g. pre-prompt sanitization filters, Bedrock guardrail, Bedrock tools that just need turned on. as general data parsing tool for apps):
Specific data fields (e.g. PHI, SSN, social, DOB, sensitive PII inc ethnicity)
General personal data filter (address, phone, email)
**(low hanging fruit) 1 week POC Generate a privacy assessment report (e.g. DPIA), based on structured template, with several documents as unstructu
file_created_at
2025-11-11T18:56:44+00:00
file_modified_at
2025-12-08T14:41:57+00:00
created_by
Paul Franke
modified_by
Justin Wray
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