One Article Review
| Source |  AlienVault Lab Blog |
|---|---|
| Identifiant | 8291507 |
| Date de publication | 2022-12-15 11:00:00 (vue: 2022-12-15 11:05:51) |
| Titre | Dark Data: What is it? How can you best utilize it? |
| Texte | The content of this post is solely the responsibility of the author. AT&T does not adopt or endorse any of the views, positions, or information provided by the author in this article. Data continues to be a valuable asset for an organization and plays a crucial role in making operational and strategic business decisions. With the growth of hybrid, private, and multi-cloud models, much of the data is stored on these platforms and becomes vulnerable to malicious activities and potential data leaks. Amid the vast volume of data, some of the data remains unknown, untapped, and unused with an organization's architecture. This dark data is generated by users' daily online interactions between several devices and systems. Dark data might seem like a scary term, but it isn't, though it poses some risks. Since its percentage of data is rising more quickly than organizational data, business organizations are getting concerned about it. Hence, to grasp what dark data is and what issues it signifies, it's essential to understand it from a broader perspective. What Is dark data? Dark data is the type of organizational data whose value is not identified; hence, it can be crucial business data or useless data. A research report published by BigID reveals that 84% of organizations are seriously concerned about dark data. This data consists of the additional information collected and stored during daily business activities. But perhaps to your surprise, the organization may be unaware of the dark data and typically doesn't use it. Dark data tends to be unstructured data that contains sensitive and unclassified information. The research report further reveals that eight out of ten organizations consider unstructured data the most critical to handle and secure. Dark data can be classified as follows: Emails, images, audio, video, and social media posts. Application trials including API caches and encryption keys such as VPN or SSH support. Data stored in overlooked virtual images activated or installed in local or cloud infrastructure. Forgotten unstructured data created on various database engines a long time ago. Customers and the company's employees own data on the desktop and mobile devices. The hidden data file in a file system can be in the form of old pictures, scanned documents, pdf forms, notes on MS Word documents, and signed files. Dark data might seem benign, but it holds most of the organization's information. Thus, it can pose significant security risks if it falls into the wrong hands, like leaking a company's sensitive data and damaging its industry reputation. This is particularly alarming for organisations that do not use a reliable VPN or any other security tools to ensure data privacy and safety. How can you utilize dark data to help your business? Dark data seems challenging to handle and involves lengthy manual processes, but companies need to automate these processes. Technological advancements such as the use of AI have made it easier for companies to explore and process unstructured data. Another important use of dark data is its role in boosting AI-powered solutions. As more and more data exists, the information that AI can analyse to produce even deeper insights. Alongside Artificial Intelligence, you can also use Machine Learning technology to discover untapped and unused data and insights. These insights might help organizations make more informed decisions regarding incoming data. Also, it guides them toward taking practical steps in response to their data. Implementing AI and ML systems needs internal structural changes for businesses, costing organizations a great deal of time and money. H |
| Notes | ★★★ |
| Envoyé | Oui |
| Condensat | if about above access accessing accurate activated activities add additional adopt advancements ago alarming all allows along alongside also amid analyse analyses analysing angry anomaly another any anytime api application apply appropriate architecture are around article artificial assessment asset assets assurance at&t attack attacks audio audits author automate away be unaware because become becomes behaviour behind benefits benign besides best between big bigid boosting breach breaching bring brings broader business business; businesses but caches call can capable categorized cause causes challenge challenges challenging chances changes choice classified classifying clients cloud collected companies company competitors compliance compliant concerned confusion consider consists constructively contains content continues control correct cost costing costs create created creating critical crucial customer customers cyber cyberattacks cybersecurity daily damaging dark data data: database day deal decides decisions deeper department desktop despite detect detection developing devices different discover discussed documents does doesn don drawback dss during each easier easily efficiently eight emails employees encryption endorse engines ensure enterprises environment era errors essential evaluation even every exists expanding experience explore extra extremely face fail fall falls file files final find fines follows: forgotten form forms from function further gdpr generated geolocation getting give gives goes going good governance governing grasp great greater growth guides hacking handle hands has have havoc hefty help helpful helps hence here hidden high hipaa hold holds house how however hybrid idea identified; ignore images implement implementing important improve improving incident include includes including incoming increase increased increasing individuals industry information informed infrastructure insight insights installed instance instead intelligence interactions internal invest investment involves iot isn issues it: its itself keys kind know later lawsuits layer lead leadership leaking leaks learning legal lengthy level like likely limited local log long looks loopholes lost lots machine made make making malicious management managers mandates manner manual may media might mobile models money more moreover most much multi multiple must need needs new not notch notes now occurs old one online operational opportunities opportunity organisations organization organizational organizations organized other out overall overlooked own owns particularly patterns pays pci pdf people percentage perform perhaps permission person perspective pictures pieces plan platforms plays policies poorly pose poses positions possible post posts potential powered practical practice preferences prevent privacy private process processes processing produce produces productive productivity protecting protection prove provide provided provider published puts putting quality quickly rate records reducing regarding regret regular regulation regulations regulators regulatory relationships reliable remain remains report reputation research response responsibility result results retain retention retrieve return reuse reveal reveals revenue rising risk risks robust role safety sanctions scanned scary secure security seem seems senior sensitive seriously server servers set several shifting short should show signed significant signifies simply since situation social solely solutions some soon; ssh standards steps store stored strategic strategies streamlining strong structural structuring success such support surface surprise system systems take taking task technological technology ten tends term than that them themselves these though thoughts threat thus time tools top tough toward traffic trend trials type types typically unauthorized unclassified understand understanding unknown unless unorganized unprecedented unrelated unstructured untapped unused uphill use used useless user users using utilize valuable value various vast verge |
| Tags | Data Breach Threat Guideline Prediction |
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