Companies need capabilities for identifying data assets and relationships, assessing data growth and implementing tiered storage strategies-capabilities that information governance can provide. It is important to classify enterprise data, understand data relationships and define service levels. Database archiving has proven effective in managing continued application data growth especially when it is combined with data discovery.
When it comes to cybersecurity, you can only defend what you can see. Organizations continue to suffer breaches, oftentimes because they do not have continuous, real-time visibility of all their critical assets. With more data and applications moving to the cloud, IoT and other emerging technologies, the attack surface continues to expand, giving adversaries more blind spots to leverage.
Watch a webinar with SANS where we examine how to:
Discover, classify and profile assets and network communications
Detect threats and decode content in real-time at wire speed
Hunt for unknown threats via rich, indexable metadata
Alter your terrain and attack surface with deception to slow down attackers
By knowing your cyber terrain and increasing the risk of detection and cost to the adversary, you can gain a decisive advantage.
A big part of GDPR compliance will focus on how data is collected going forward. But a substantial emphasis will fall on the data businesses already hold. With many mainframes containing generations-old data, a manual data audit is completely unrealistic. That’s where CA comes in. CA Data Content Discovery enables organizations to find, classify and protect mission essential mainframe data—three valuable steps toward achieving GDPR compliance.
Component Content Management: A New Paradigm in Intelligent Content Services
While technology has changed the world, the way that companies manage information has inherently stayed the same. The advent of near-ubiquitous connectivity among applications and machines has resulted in a data deluge that will fundamentally alter the landscape of content management. From mobile devices to intelligent machines, the volume and sophistication of data have surpassed the ability of humans to manage it with outdated methods of collection, processing, storage, and analysis. The opportunity afforded by the advent of artificial intelligence (AI) has stimulated the market to search for a better way to capture, classify, and analyze this data in its journey to digital transformation (DX). The paradigm of document-based information management has proven to be a challenge in finding, reusing, protecting, and extracting value from data in real time. Legacy systems may struggle with fragmented information
Today, deep learning is at the forefront of most machine learning implementations across a broad set of business verticals. Driven by the highly flexible nature of neural networks, the boundary of what is possible has been pushed to a point where neural networks outperform humans in a variety of tasks, such as classifying objects in images or mastering video games in a matter of hours. This guide outlines the end-to-end deep learning process implemented on Amazon Web Services (AWS). We discuss challenges in executing deep learning projects, highlight the latest and greatest technology and infrastructure offered by AWS, and provide architectural guidance and best practices along the way.
This paper is intended for deep learning research scientists, deep learning engineers, data scientists, data engineers, technical product managers, and engineering leaders.
The key tenets of protecting data privacy are: knowing where your data resides, understanding who has access to the data and what are they doing with it, and knowing how the data is being used internally that may result in non-compliance. Additionally, most compliance projects are major, company-wide initiatives and must be managed properly to meet deadlines and keep costs in check.
Different types of data have different data retention requirements. In establishing information governance and database archiving policies, take a holistic approach by understanding where the data exists, classifying the data, and archiving the data. IBM InfoSphere Optim™ Archive solution can help enterprises manage and support data retention policies by archiving historical data and storing that data in its original business context, all while controlling growing data volumes and improving application performance. This approach helps support long-term data retention by archiving data in a way that allows it to be accessed independently of the original application.