Expands and protects certain data items on death certificates of decedents; provides surviving family members greater access to crashed vehicles. It shall not contain a space to include the Social Security number of the person being charged; this provision does not affect the right a person may have under other law to use the person's Social Security number as the person's driver's license number. Missouri Requires court clerks to remove Social Security numbers from certain family court filings. Judgments and other orders in divorce, legal separation, child support, and child custody cases will not contain any Social Security numbers; the name and address of the current employer; and the name and date of birth of each child.
National Security-Related Applications of Artificial Intelligence Introduction There are a number of direct applications of AI relevant for national security purposes, both in the United States and elsewhere. Take X and add AI. Included below are some examples in cybersecurity, information security, economic and financial tools of statecraft, defense, intelligence, homeland security, diplomacy, and development.
This is not intended as a comprehensive list of all possible uses of AI in these fields.
Rather, these are merely intended as illustrative examples to help those in the national security community begin to think through some uses of this evolving technology. The next section covers how broader AI-driven economic and societal changes could affect international security.
Cybersecurity The cyber domain represents a prominent potential usage arena for AI, something senior leaders have expressed in recent years.
Each system was capable of automatically discovering and exploiting cyber vulnerabilities in its opponents while patching its own vulnerabilities and defending itself from external cyberattacks. The systems in the first Cyber Grand Challenge used rule-based programming and did not make significant use of machine learning.
Were a similar competition to be held today, machine learning would likely play a much larger role. Below are several illustrative applications of machine learning in the cybersecurity domain that could be especially impactful for the international security environment.
Increased Automation and Reduced Labor Requirements Cyber surveillance has tended to be less labor-intensive than the traditional human surveillance methods that it has augmented or replaced.
The increased use of machine learning could accelerate this trend, potentially putting sophisticated cyber capabilities that would normally require large corporation or nation-state level resources within the reach of smaller organizations or even individuals.
Narrow AI will increase the capabilities available to such actors, lowering the bar for attacks by individuals and non-state groups and increasing the scale of potential attacks for all actors. The machine learning approach allows the system to learn from prior experience in order to predict which locations in files are most likely to be susceptible to different types of fuzzing mutations, and hence malicious inputs.
This approach will be useful in both cyber defense detecting and protecting and cyber offense detecting and exploiting.
Automated Red-teaming and Software Verification and Validation While there is understandable attention given to new vulnerability discovery, many cyber attacks exploit older, well-known vulnerabilities that system designers have simply failed to secure.
SQL-injection, for example, is a decades-old attack technique to which many new software systems still fall prey.
AI technology could be used to develop new verification and validation systems that can automatically test software for known cyber vulnerabilities before the new software is operationally deployed. Email phishing to trick users into revealing their passwords is a well-known example.
The most effective phishing attacks are human-customized to target the specific victim aka spear-phishing attacks — for instance, by impersonating their coworkers, family members, or specific online services that they use.
AI technology offers the potential to automate this target customization, matching targeting data to the phishing message and thereby increasing the effectiveness of social engineering attacks.Social Security Number Legislation.
Last updated: May 7, NCSL Staff Contact: Heather Morton, Denver, () Established in by the Social Security Administration, Social Security numbers (SSNs) were originally used to track earnings and eligibility for Social Security .
Why using Social Security numbers for identification is risky and stupid. The system works particularly well for people born in small states, which have only a few possible area numbers.
(For. On December 1, , as part of the publicity campaign for the new program, Joseph L. Fay of the Social Security Administration selected a record from the top of the first stack of 1, records and announced that the first Social Security number in history was assigned to John David Sweeney, Jr., of New Rochelle, New York.
The Social Security Act (P.L. ) is enacted.
It did not expressly mention the use of SSNs, but it authorized the creation of some type of record keeping scheme. The Social Security number (SSN) was created in for the sole purpose of tracking the earnings histories of U.S. workers, for use in determining Social Security benefit entitlement and computing benefit levels.
The Social Security number (SSN) was created in for the sole purpose of tracking the earnings histories of U.S. workers, for use in determining Social Security benefit entitlement and computing benefit ashio-midori.com then, use of the SSN has expanded substantially. Today the SSN may be the most commonly used numbering system in the United States.
As of December , the Social Security.