Data Loss Prevention - Tips and Techniques for the Mitigating Data Loss in the Cloud
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About this course:
In this webinar Joseph Holbrook, a Cloud Native Subject Matter Expert(SME) will provide insight into “Data Loss Prevention Tips and Techniques to mitigate Data Loss in the Cloud” to provide for a well fortified cloud environment. Utilizing the common but also not so common tips and techniques such as using an “ AUTOMATED validated DLP policy” can mitigate common human errors and has been proven to reduce exposure.
The top 5 Data Loss Prevention tools for On Premises users need to use to help reduce company risk to data loss exposure. Data loss prevention software detects, prevents and monitors potential data breaches and data ex-filtration transmissions. As an added bonus, attendees will receive a FREE DLP Prevention Checklist that can be used immediately and Data Loss Prevention Practice questions for preparing the CISSP exam!
- According to Risk Based Security there over 3,813 data breaches that were reported through June 30, exposing over 4.1 billion records. Compared to the midyear of 2018, the number of reported breaches was up 54% and the number of exposed records was up 52%.
- Marriott’s data loss - In November 2018, Marriott International stated they had a major data loss of 500 million customers and that cyber thieves had stolen data such as contact info, passport number and credit cards.
- Establishing your compliance requirements can provide for an immediate DLP baseline and provide immediate value for your DLP project.
- DLP Software can even detect phishing & malware threats, prevent account takeovers, personal data loss and even monitor for unsanctioned SaaS apps
- There are no prerequisites required for this course
|Learning Style||Self-Paced Learning|
|Course Duration||1 Hour|
(Data Science Enthusiast)
Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.
Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.
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