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AI’s ability to extract information from large document troves will help whistleblowers provide more information to the SEC faster, lowering the bar for reporting potential violations and opening a floodgate of new tips. The SEC already, as Platt documented, relies on a few firms to prioritize their investigative agenda.
Category Risk Modeling, Quantitative RiskRisk Level. avoid fire), explicit decision trees in a document (e.g. ePHI means high risk), or they could be decision rules we encode into algorithms, like those used in software for malware and virus detection (e.g. What are heuristics?”
Huge arrays of unstructured data utilized and modified by many users as well as the ever-growing complexity of attacks, lead to the fact that the usual means of protecting the perimeter of a corporate network no longer meet current information security requirements. Information tends to spread at a rate proportional to the number of users.
This includes making sure only the right people are looking at the right data, only the right people are logging into the right systems, that services are available with enough 9’s, that they’re responsive, etc. I think the answer is: More Electrician-types (tradespeople connecting things according to documented specs).
Dashboards Splunk, Snowflake, and other vendors have the eyeballs of the dataanalysts and the SOC. How are they documented? In our case, we add value by reducing the risk of especially advanced attacks. What is the value of reducing the risks of potentially successful attacks? Who built them? How are theytested?
With the rise of AI, new risks to cybersecurity have appeared. AI-generated fakes, or highly precise fabricated documents, media, and digital artefacts, are one of the most important challenges for DFIR practitioners. Detecting metadata : In addition to EXIF data, other types of metadata can be embedded within the image file.
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