Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

While it might seem initially abstract, sustainability and data security are symbiotic. Allow me to explain. Regardless of the action or industry, critical data touches everything. Whether data is being used in pharmaceutical research, financial records, or intellectual property, securing it is the common imperative. This is especially true as it moves through various access points, the cloud, applications, the web, and various other transactions.

With this short guide we will explain how you can add data loss prevention (DLP) as code to communications services like Twilio and SendGrid in a few lines of code, helping ensure your customer communications remain compliant.

Being a business leader in today’s business environment is no walk in the park. With over 2,200 cybersecurity attacks occurring daily, the task of protecting valuable business data adds an extra layer of complexity to your plate. How do you safeguard your crucial data, ensuring it’s not lost or compromised? You need a comprehensive Data Loss Prevention (DLP) strategy fortified by the best IT security software.

When looking for programmatic secrets, it’s not easy to figure out what is truly sensitive and how high-risk it is. There are many different types of secrets and credentials, and the context makes a difference. For example, there could be public URLs with tokens in them, public UUIDs, or credentials used in frontend code — these could all be considered API keys or secrets, but not necessarily at the same degree of sensitivity/severity as something like AWS credentials.

A security violation in the form of a data breach can create costly damage to a company's reputation. But what exactly is a data breach? The European Commission has divided data breaches into three distinct categories — confidentiality breaches, integrity breaches, and availability breaches: In this article, you'll learn more about what a data breach is and how you can prevent data breaches when designing and developing your software.

Since the onset of the pandemic in 2020, global concern for data security and privacy has skyrocketed like a dazzling display of fireworks on New Year’s Eve. With an ever-increasing number of people utilizing online services and sharing their personal information on websites to engage in e-commerce transactions, the infrastructure for collecting and safeguarding consumer data has become of paramount importance.

When a hacker breaches a network or system, data exfiltration often follows. But what is data exfiltration and how can you prevent it?

Generative Artificial Intelligence (AI) has revolutionized various fields, from creative arts to content generation. However, as this technology becomes more prevalent, it raises important considerations regarding data privacy and confidentiality. In this blog post, we will delve into the implications of Generative AI on data privacy and explore the role of Data Leak Prevention (DLP) solutions in mitigating potential risks.

Cyber threats are a growing concern for organizations of all sizes. Data breaches, malware infections, and ransomware attacks can severely disrupt operations, including financial loss, reputational damage, and legal liabilities. As a result, it is essential to proactively monitor your environment and identify malicious activity to detect threats before they can cause significant damage.

There’s no denying it, public cloud is here to stay and there’s a pretty good chance that your company is running some workloads on Amazon Web Services.

As organizations increasingly adopt cloud services and applications, securing access to these services becomes crucial to protect sensitive data and maintain compliance. Cloud Access Security Brokers (CASBs) have emerged as a key component in providing comprehensive visibility and control over cloud services.

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. In today's digital age, sensitive information is constantly being shared and transmitted over various electronic devices and networks.

Last year, we launched Cato DLP to great success. It was the first DLP engine that could protect data across all enterprise applications without the need for complex, cumbersome DLP rules. Since then, we have been improving the DLP engine and adding key capabilities, including user-defined data types for increased control and integration with Microsoft Information Protection (MIP) to immediately apply sensitivity labels to your DLP policy. Let’s take a closer look.

Attack surfaces are a fundamental concept within information security. However, attack surfaces can be constituted of different things. For example, some formulations of an attack surface include not just software and hardware, but the people using them. In this post, we’re going to cover four common types of attack surface, discuss how you should think about the risks associated with each type, and best practices for addressing these risks.

Cybersecurity, privacy and data flows will be key topics at this year’s G7 in Japan. Against this backdrop, it has been a year since last year’s amendments to the Act on Protection of Personal Information (APPI) introduced revised data breach reporting and cross-border data flow rules. Meanwhile, developments in the Asia-Pacific (APAC) region have introduced data localization obligations in a variety of countries.

For many companies, a business credit card is part of the organization’s lifeblood. As such access to it must be vigilantly maintained. One potential area of risk is employees sharing credit card details in collaborative SaaS applications like Slack, where these details are at significant risk of being exposed to unauthorized parties.

Security professionals were once confident that the valuable data they protected was safely tucked away inside heavily fortified data centers. But as businesses of all sizes undergo digital transformation, moving their data to the cloud and across numerous distributed locations, the demands placed on legacy data protection systems have changed drastically.

Nightfall’s Cloud DLP platform helps you limit access to sensitive data, by protecting the data where it is stored. With Nightfall you can redact, delete attachments, or make the data private in one click or via automated actions.

One of Canada’s largest credit unions, Coast Capital Savings Credit Union (CSS), with over 50 branches across the country, needed to protect critical PII data stored in their AWS cloud environments, Amazon Redshift, AWS EMR, and AWS S3. CSS had to meet Canadian regulatory standards for compliance in 2021, including the Personal Information Electronic Documents Act (PIPEDA), along with a data transformation initiative surrounding its architecture.

The proliferation of sensitive data continues to grow unabated, particularly for support agents using Zendesk. The Zendesk’s CX Trends 2022 Report found that ticket volume had increased across all channels, with webform/email up 10% YoY and chat up 17% YoY.

We’ve all seen it in the movies: The chief of security declares that no one could ever steal the “Famous Jewel” because it’s protected by thick glass, an impenetrable system of motion-detecting lasers, and, finally, a weight-sensitive sensor. Cut to the next scene, where a thief, dressed in sleek black, zip-lines from an open skylight, shorts the lasers, scores the glass, and with split-second timing, swaps the jewel for a replica of equal weight.