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The following article was first posted in the Insights section of Harris Beach Murtha’s website. It is reposted here with permission.
In its ongoing efforts to revamp the H-1B visa status framework, the U.S. Department of Homeland Security issued a Notice of Proposed Rulemaking on Sept. 24, 2025, proposing a regulatory rule to change the process by which cap-subject H-1B status applicants are selected by U.S. Citizenship and Immigration Services (USCIS) for eligibility to file H-1B petitions in its annual lottery.
This substantive change proposes a weighted lottery selection system whereby H-1B beneficiaries offered the highest wages are given the highest likelihood of being chosen in the lottery and afforded the opportunity to subsequently petition for H-1B work status.
This system is a marked departure from the current system, in which there is generally no special preference given to H-1B lottery entrants.
The H-1B visa status classification is the most common visa type used for U.S. employers to hire and employ foreign nationals for temporary employment. This status is available to those hired to be employed and perform services in a “specialty occupation.”
This term is defined in the relevant immigration regulations as an occupation that:
“… requires theoretical and practical application of a body of highly specialized knowledge in fields of human endeavor including, but not limited to, architecture, engineering, mathematics, physical sciences, social sciences, medicine and health, education, business specialties, accounting, law, theology, and the arts, and which requires the attainment of a bachelor’s degree or higher in a specific specialty, or its equivalent, as a minimum for entry into the occupation in the United States.” [8 U.S.C. § 214.2(h)(4)(ii)]
To implement and set limits upon the H-1B program, Congress set an annual quota restricting its use.
The current framework caps the annual quota for new H-1B petitions for those who have not been granted H-1B status during the previous six years at 65,000.
The H-1B cap numbers have not been updated for many years and have not accounted for population growth or the current needs of the U.S. economy.
An additional 20,000 slots are also allocated for those foreign national beneficiaries who have earned the minimum of a master’s degree from a qualifying U.S. educational institution.
Due to political constraints, the H-1B cap numbers have not been updated for many years and have therefore not accounted for population growth or the current needs of the U.S. economy.
Accordingly, for many years, the annual H-1B quota has been filled nearly instantaneously upon the commencement of the filing eligibility period.
This situation has led to the creation of the H-1B cap lottery system, wherein a random lottery system is used annually at the outset/opening of each H-1B cap season to determine which applicants will fill the available cap slots and be afforded the opportunity to petition for, and gain, H-1B status each year.
Until now, no special consideration in the annual cap lottery was given regarding the compensation level for each offered H-1B position.
Indeed, the plain language of the controlling Immigration and Nationality Act statute states that the H-1B cap slots conferring H-1B eligibility should be “issued … in the order in which petitions are filed for such visas or status” (thereby permitting the cap lottery system to be used at the commencement of each year’s quota system implementation in a random nature, and not by limiting/favoring the availability of the H-1B program to those employers who pay the highest compensation).
In contrast, the proposed new DHS rule drastically changes the way in which those who registered for the H-1B cap lottery are selected to have the opportunity to petition for H-1B status.
It instead institutes a new wage-based selection process system where the selection of H-1B registrations are prioritized/weighted based upon the salary compensation level the employer plans to pay the H-1B worker.
For instance, there are four wage levels currently used by the U.S. Department of Labor to categorize H-1B specialty occupations.
The selection of H-1B registrations are prioritized/weighted based upon salary compensation level.
This new rule utilizes that mechanism to prioritize each submitted lottery registration in terms of its likelihood to be chosen as a lottery winner.
In effect, those H-1B beneficiaries to be paid at the highest Level 4 wage will possess four times the chance of being selected in the lottery than those being paid at the Level 1 “entry-level,” those with Level 3 qualifying wage compensation will have three times the chance of success as Level 1 applicants, etc.
As a result, USCIS has acknowledged the probability of Level 1 workers being selected to be eligible to file an H-1B cap-subject petition will decrease by 48%!
Immigration advocates argue this proposed rule/regulation violates Congressional intent regarding the promulgation and implementation of the H-1B program, as Congress did not intend to limit the program’s use to employers with the ability to pay the highest wages.
Indeed, it can be rationally said the U.S immigration system as a whole was not created for, nor intended to be, a plutocratic type of system favoring the wealthy.
Immigration advocates claim this change will have a substantial negative impact on a wide range of U.S. employers and industries, including the healthcare and technology fields.
Additionally, they argue the new system will limit the ability of smaller businesses and startups to use the program to foster innovation and growth, thereby unnecessarily hindering the potential success of their business efforts.
Therefore, they say this consequence could ultimately hurt, not help, the U.S. economy.
Conversely, large corporations that can afford to pay the higher wages are enthused by this change, as it will undoubtedly lead to higher percentages of their H-1B cap lottery entrants being selected as winners, affording them the opportunity to employ more of these workers each year than prior lottery results have yielded.
The new system will limit the ability of smaller businesses and startups to use the program to foster innovation and growth.
In terms of what comes next, the 30-day comment period required by the Administrative Procedure Act ended in late October.
During this period, comments were submitted by immigration advocates, including the American Immigration Lawyers Association, in opposition to the proposed rule.
AILA requested that, at a minimum, this rule be delayed in its implementation and not used for the upcoming fiscal year 2027 cap filing season (the lottery for which will be administered in March of 2026).
Judicial action is also being contemplated by an array of immigration advocacy groups, but no lawsuit contesting the measure has been filed as of this writing.
Therefore, barring these types of developments to pause or prevent its implementation, the rule is likely to complete its required regulatory process in time to be implemented for the next H-1B cap lottery process in March.
Harris Beach Murtha’s Immigration Practice Group will continue monitoring this and related H-1B issues and update the public regarding future developments.
About the author: David Jacobson is a Harris Beach Murtha member who focuses his practice on employment-based immigration matters for corporate clients in various industries, including financial services, pharmaceuticals, media, information technology, real estate, construction, chemical and environmental engineering, market research, and advertising.
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AWS Blogs
January 2025: This post was reviewed and updated to use Git repository instead of AWS CodeCommit, which is no longer available to new customers.
Several factors can make remediating security findings challenging. First, the sheer volume and complexity of findings can overwhelm security teams, leading to delays in addressing critical issues. Findings often require a deep understanding of AWS services and configurations and require many cycles for validation, making it more difficult for less experienced teams to remediate issues effectively. Some findings might require coordination across multiple teams or departments, leading to communication challenges and delays in implementing fixes. Finally, the dynamic nature of cloud environments means that new security findings can appear rapidly and constantly, requiring a more effective and scalable solution to remediate findings.
In this post, we will harness the power of generative artificial intelligence (AI) and Amazon Bedrock to help organizations simplify and effectively manage remediation of security control findings. By using Agents for Amazon Bedrock with action groups and Amazon Bedrock Knowledge Bases, you can now create automations with AWS Systems Manager Automation (for services that support automations with AWS Systems Manager) and deploy them into AWS accounts. Thus, by following a programmatic continuous integration and development (CI/CD) approach, you can scale better and remediate security findings promptly.
This solution follows prescriptive guidance for automating remediation for AWS Security Hub standard findings. Before delving into the deployment, let’s review the key steps of the solution architecture, as shown in the following figure.
Figure 1 : AWS Security Hub control remediation using Amazon Bedrock and AWS Systems Manager
Note: The folder structure of the Git repository is based on AWS services, and there is a parameters file under each parent AWS service folder.
In this post, we focus on remediating the following two example security findings:
The example findings demonstrate the two potential paths the actions group can take for remediation. It also showcases the capabilities of action groups with Retrieval Augmented Generation (RAG) and how you can use Amazon Bedrock Knowledge Bases to automate security remediation.
For the first finding, AWS has an existing Systems Manager runbook to remediate the S3.5 finding. The solution uses the existing runbook (through a knowledge base) and renders an AWS CloudFormation template as automation.
The second finding has no AWS provided runbook or playbook. The solution will generate a CloudFormation template that creates an AWS Systems Manager document to remediate the DMS.1 finding.
Below are the prerequisites that are needed before you can deploy the solution.
There are four main steps in order to deploy the solution.
Configuring a knowledge base enables your Amazon Bedrock agents to access a repository of information for AWS account provisioning. Follow these steps to set up your knowledge base.
Prepare the data sources:
Create the knowledge base:
Note: After successful creation, copy the knowledge base ID because you will need to reference it in the next step.
Sync the data source:
IMPORTANT: The following steps are required for GitHub integration. If you choose to use a different source control provider, you may do so, but you will need to edit the committer class accordingly. This is crucial for the proper functioning of the solution with your chosen source control system.
GitHub Setup
This command will deploy all the necessary resources, including the Lambda function, the CodePipeline, and the Amazon SNS notification.
Create an action group linked to the Lambda function that was created in the CDK app. This action group is launched by the agent after the user inputs the Security Hub finding ID or finding title, and outputs a CloudFormation template in the Code Commit repository.
Note: For this solution, openapischema.json is provided to you in the Git repository. Upload the JSON into the S3 bucket created in Step 1 and reference the S3 URI when selecting the API schema in this step.
In order to validate the solution, follow the below steps :

Figure 2 : AWS Security Hub finding title
If a finding already has an AWS remediation runbook available, the agent will output its details. That is, it will not create a new runbook. When automation through a Systems Manager runbook isn’t possible, the agent will output a message similar to “Unable to automate remediation for this finding.”
Figure 3 : An example Bedrock Agent Interaction
Note: Bedrock_Generated_Template_Name refers to the name of the YAML file that has been output by Amazon Bedrock.
Figure 4 : An example Bedrock generated YAML file

To avoid unnecessary charges, delete the resources created during testing. To delete the resources, perform the following steps:
The integration of generative AI for remediating security findings is an effective approach, allowing SecOps teams to scale better and remediate findings in a timely manner. Using the generative AI capabilities of Amazon Bedrock alongside AWS services such as AWS Security Hub and automation, a capability of AWS Systems Manager, allows organizations to quickly remediate security findings by building automations that align with best practices while minimizing development effort. This approach not only streamlines security operations but also embeds a CI/CD approach for remediating security findings.
The solution in this post equips you with a plausible pattern of AWS Security Hub and AWS Systems Manager integrated with Amazon Bedrock, deployment code, and instructions to help remediate security findings efficiently and securely according to AWS best practices.
Ready to start your cloud migration process with generative AI in Amazon Bedrock? Begin by exploring the Amazon Bedrock User Guide to understand how you can use Amazon Bedrock to streamline your organization’s cloud journey. For further assistance and expertise, consider using AWS Professional Services to help you accelerate remediating AWS Security Hub findings and maximize the benefits of Amazon Bedrock.
Shiva Vaidyanathan is a Principal Cloud Architect at AWS. He provides technical guidance for customers ensuring their success on AWS. His primary expertise include Migrations, Security, GenAI and works towards making AWS cloud adoption simpler for everyone. Prior to joining AWS, he has worked on several NSF funded research initiatives on performing secure computing in public cloud infrastructures. He holds a MS in Computer Science from Rutgers University and a MS in Electrical Engineering from New York University.
Huzaifa Zainuddin is a Senior Cloud Infrastructure Architect at AWS, specializing in designing, deploying, and scaling cloud solutions for a diverse range of clients. With a deep expertise in cloud infrastructure and a passion for leveraging the latest AWS technologies, he is eager to help customers embrace generative AI by building innovative automations that drive operational efficiency. Outside of work, Huzaifa enjoys traveling, cycling, and exploring the evolving landscape of AI.
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Winning numbers drawn in Tuesday’s Iowa Pick 3 Evening  CTPost
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The jackpot for Friday’s Mega Millions drawing has grown to the eighth-highest in the game’s history. At $843 million, the prize is fueling ticket sales among regular lottery players in Michigan and others dreaming about what they’d do if they became overnight multimillionaires.
The massive jackpot, which has a cash value of $391.7 million, grew after no one matched all six numbers in Tuesday’s drawing: the white balls 11, 14, 17, 50 and 57, plus the gold Mega Ball 6.
To play the lottery game in Michigan, all in-store and online tickets must be bought before 9:45 p.m.
You can buy Powerball tickets both online and at local Michigan Lottery retailers, such as grocery stores, convenient stores and gas stations. You can sign up and play online here.
Tickets are $5 each, with a built-in multiplier feature added in April that automatically increases non-jackpot prizes by 2, 3, 4, 5, or 10 times. Previously, the base ticket was $2, and the multiplier was an optional $1 add-on in most states.
The rule changes were designed to produce larger, faster-growing jackpots and more non-jackpot prizes The changes slightly improved the odds of winning the jackpot, from 1 in 302,575,350 to 1 in 290,472,336, achieved by decreasing the gold Mega Ball pool from 25 to 24 numbers.
The odds of winning any prize also improved, from 1 in 24 to 1 in 23.
Friday’s drawing is the 38th since the jackpot was last won in Virginia on June 27. It’s the longest streak without a jackpot winner since the game began in 2002, and the previous record of 37 drawings was set in January 2021, when a $1.050 billion jackpot was won in Michigan.
Mega Millions said 606,046 winning tickets valued at more than $12.2 million were sold across all prize tiers in Tuesday’s drawing, including 12 tickets that matched four white balls plus the Mega Ball to win third-tier prizes ranging from $20,000 to $40,000, depending on their multiplier. Those tickets were sold in Arkansas, California, Colorado, Florida, Michigan, New Hampshire, New Jersey, Pennsylvania, Texas, Virginia and Wyoming.
In this jackpot run to date, there have been almost 11.7 million winning tickets at all levels, with total prizes exceeding $274 million due to significant enhancements to lower-tier prizes in changes made to the game last April, Mega Millions said.
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FanDuel Casino Doubles Jackpot Chances for Michigan Players  PlayMichigan
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$5 million lottery winner busted for allegedly stealing from Millbrae city job  KTVU
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It is a rollover on the Lotto tonight and we'll be bringing you the numbers on the dot as they come in.
There is a £5 million jackpot up for grabs in the National Lottery game and a top prize of £500,000 can also be won on the Thunderball.
The Lotto draw is at 8pm and the Thunderball draw is at 8.15pm.
We will bring you the results of the draws right here as soon as they're available, which you can see below. Good luck!
Get the results:
6, 11, 13, 19, 39 and the Thunderball is 5.
5
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39
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It is less than 15 minutes until the Thunderball results. We will be bringing you all the numbers, live.
2, 15, 20, 42, 43 and 59. The bonus ball is 16.
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43
Five minutes to go until the lottery results, we will be bringing yoy live updates.
15 minutes until the live Lotto draw.
It is half an hour to go until the live Lotto results. We will be bringing you all the numbers.

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