Learn about the benefits of implementing the principle of least privilege in Data Protection 101, our series on the fundamentals of information security.

Learn about approaches to data protection for data in transit vs. data at rest in Data Protection 101, our series on the fundamentals of data security.

CPRA enforcement is only months away, so here’s everything that organizations need to know about how it differs from the CCPA, how CCPA compliance will be different moving forward, and whether or not the changes will apply to your organization.

Learn how a malware gang’s tactics are being used against them, how the Department of Homeland Security is facing new threats head-on, an ambitious project to create a more secure OS, and more—all in this week’s Friday Five!

Financial services companies will have to retool their cybersecurity policies when the new version of NYDFS Cybersecurity Regulation, including a core risk assessment requirement, goes live.

The proliferation of cloud computing has heightened the need for organizations to monitor and manage the safe use of cloud services. Cloud access security brokers, or CASBs, provide the necessary security features to protect cloud-based resources as they’re accessed while also detecting threats and controlling data that flows through the cloud. What Are the 4 Pillars of Cloud Access Security Brokers (CASBs)? A cloud access security broker is either an on-premise or cloud-hosted software strategically placed between the service consumers and the cloud service providers. Its primary role is to enforce security policies with features like malware detection, encryption, authentication, credential mapping, tokenization, and regulatory compliance. In essence, a CASB is an added layer of security that acts like a firewall. It also enables organizations to extend the reach of their security controls beyond network boundaries. Consequently, this empowers CISO/CIOs to protect mission-critical data in their enterprise, like intellectual property (IP), personally identifiable information (PII), and comply with payment card industry (PCI) standards. To accomplish this, a CASB is based on foundational building blocks, such as the following: 1. Data Security With its on-demand computing, the cloud has boosted data movement and collaboration at a distance. However, this seamless interaction with data has made it more vulnerable, especially when it exits outside the network perimeter. This widened attack surface comes at a considerable cost to businesses that must protect sensitive data such as customer information, intellectual property, and trade secrets. To strengthen data security, a CASB is equipped with sophisticated tools to minimize the risk of costly leaks. These typically encompass a range of data protection and monitoring tools, including cloud data loss prevention (DLP) mechanisms, to protect sensitive data and battle shadow IT. In the CASB arsenal, other tools to prevent data leaks include encryption mechanisms, information rights management, authentication & authorization, access control, and tokenization. 2. Visibility Visibility is paramount if organizations are going to identify and protect sensitive data, whether it’s at rest or in motion. The visibility challenge that enterprises typically struggle with is the specter of having too many employees across multiple cloud environments juggling data at various endpoints. Having a CASB enables organizations to discover all their data in use, pinpoint shadow IT, scope redundancies, evaluate license costs, and provide reports on cloud expenditures. As a result, the capabilities of a CASB can equip organizations with visibility to observe how sensitive data travels, whether in the cloud, to and from the cloud, or from cloud-to-cloud environments. 3. Compliance The importance of data and its mass migration to the cloud has underscored the need for robust personal privacy protections. With the raft of regulatory laws around securing PII passed in recent years, enterprises increasingly face complex security enforcement demands. Aside from regulations with an international scope like the General Data Protection Regulation (GDPR), enterprises in different business verticals need to monitor their compliance with laws governing their respective industry. Fortunately, CASBs are equipped for such versatility, ensuring that healthcare providers can comply with the Health Information Technology for Economic and Clinical Health (HITECH) Act and the Health Insurance Portability and Accountability Act (HIPAA); financial service organizations are in line with the Federal Financial Institutions Examination Council (FFIEC) and the Financial Industry Regulatory Authority (FINRA) and retailers are aligned with Payment Card Industry Data Security Standard (PCI DSS) compliance. Traditional security systems are usually insufficient to monitor enforcement between users and cloud-based systems, especially across multiple locations and devices. Having a CASB in place helps facilitate cloud governance and risk assessment by providing security teams with the appropriate guidance on resolving multiple risk areas. 4. Threat Protection With how fast data is passed through cloud-based services, organizations must proactively identify and isolate threats. Fortunately, today’s CASBs are equipped with cutting-edge technology that enables them to evolve continuously in their ability to detect anomalous behavior. Powered by intelligent automation tools and AI in the form of machine learning, CASBs can help thwart zero-day threats, ransomware, and advanced persistent threats. They can also integrate the principle of least privilege (POLP) controls to prevent attackers who have breached the network from moving laterally to access sensitive data. How Does a CASB Work? The main goal of a CASB is to secure data flowing through an organization’s IT infrastructure, both on public cloud vendors and on-premise environments. To achieve this, CASBs primarily use a three-part process: Discovery: As the name implies, discovery seeks to unearth and pinpoint all cloud applications, especially third-party services, automatically. CASBs can identify apps as well as the employees affiliated with them. Classification: CASBs use data classification to identify and prioritize data, evaluate each cloud application, and determine its security risk levels. Classification also facilitates the understanding of how an application is used, the kind of data it consumes, and how it is shared within the app. Remediation: CASBs don’t stop at identifying threats; they can also mitigate vulnerabilities after discovering the risk levels encountered in cloud services. Consequently, CASBs can leverage this information to create tailored policies to address the organization’s security requirements. They can take action automatically to fix any security violations according to policy. The Main Use Cases of CASBs While CASBs provide many security benefits, their main use case is safeguarding proprietary data like trade secrets and intellectual property in third-party, external-facing media like public cloud environments. In addition, CASBs also bridge the gap between capabilities not found in traditional firewalls and secure web gateways (SWGs). Here are the common use cases associated with having a CASB: Protect against cybersecurity threats: CASBs employ mechanisms such as continuous monitoring, threat intelligence gathering, and anomaly detection to fight against malware, ransomware, and advanced persistent threats. Threat prevention and activity monitoring: By leveraging user and entity behavior analytics, CASBs can establish a baseline of expected behavior and flag any deviation while establishing granular control of cloud usage. Boosting risk visibility: CASBs can identify high-risk vulnerabilities and accurately assess risk contextually, subsequently setting appropriate mitigation policies. Shadow IT assessment and management: CASBs offer much-needed insight into sanctioned and unsanctioned applications. Having visibility into cloud services can help uncover rogue applications while delivering a comprehensive picture of your risk profile and any security measures in place. Data loss prevention: CASBs can prevent data leakage and unauthorized access to sensitive data like proprietary information, in addition to financial, health, social security, and credit card numbers. This involves using robust user verification to control cloud-native resources, especially during collaboration and sharing, while blocking shared document downloads. Maintaining regulatory compliance: With tools like encryption, key management, and DLP, CASBs can provide sufficient protection to handle problems related to local laws and data residency – the physical or geographic location of an organization’s data or information. This can help your organization meet regulatory requirements. As a result, data is safeguarded throughout its lifecycle while meeting compliance. Configuration auditing: Improper cloud configurations can create systemic risks for organizations. Unfortunately, most cybersecurity misconfigurations are self-inflicted. A recent Gartner report pointed out that 99% of cloud security failures are due to the customer. Configuration auditing with a CASB allows you to spot improper cloud misconfigurations, default passwords, and easily compromised settings. Adaptive access control: CASBs provide flexible and contextual cloud-based access control, whether to enforce location-based or endpoint policies. How Can Fortra/Digital Guardian Secure Collaboration Help Me with a CASB? Fortra/Digital Guardian Secure Collaboration has extensive expertise working with CASBs to protect sensitive data. Digital Guardian Secure Collaboration’s capabilities are bolstered by a data-centric security model based on rights management and DLP. Learn more about cloud-based access security brokers and how we can extend file protection in the cloud.

CISA has released new guidance for Zero Trust Security and secure-by-design principles for software manufacturers, Russian hackers were linked to attacks against NATO and the EU, and more. Catch up on all the latest stories in this week's Friday Five!

Looking to bolster your Amazon Web Services (AWS) container security? Fortra's AlertLogic team has pulled together three tips to help secure your container ecosystem.

Data Encryption Algorithm Methods & Techniques Every company has sensitive data that it needs to protect, yet extracting value from your data means that you must use it, whether that means feeding it to a data analytics tool, sharing it with partners or contractors, or even simply storing it in the cloud or on a USB. While you can take steps to prevent unauthorized access to your network and sensitive data, what happens if a cyberattacker breaks through your defenses? It's considered an essential best practice for data loss prevention. What is Data Encryption? Data encryption is a widely used approach to rendering data uninterpretable should unauthorized users gain access to it. Using a data encryption algorithm, data encryption translates data from its raw, plain text form (plaintext data) — which is easily readable by anyone who accesses it — to a complex form or code (ciphertext) that's unreadable and unusable unless the user has a decryption key or password that will "decrypt" the data by translating it back to its plain text format. For example, if a cybercriminal gains access to a database containing customers' Social Security numbers, but the data is encrypted, the attacker can gain no value from it. Because they can't interpret the true Social Security numbers, they can't use the data for identity theft, and they can't sell it on the dark web. There are two primary types of data encryption algorithms: Asymmetric encryption, also known as public key encryption, which uses two keys: a public key and a private key. The public key is used to encrypt the data, and the private key is used to decrypt the data. The private key is carefully protected, shared only between the sender and receiver of the data. Symmetric encryption, which uses the same key to encrypt and decrypt data. A hash function is another method involved in data encryption. Hashing uses an algorithm to translate data of any size to a fixed length, resulting in a hash value, rather than the ciphertext produced by encryption algorithms. Hashing is used to verify that data has not been altered from its previous state during transmission. For example, if one person is sending a sensitive file to another user and the user needs to confirm the integrity of the data, the original person can send a hash value along with the data. The recipient can then calculate the hash value of the data they've received. If the data hasn't been altered, the two hash values will be the same. Data encryption enables: Authentication: Did the data come from where it claims or appears to? Integrity: Is the data unchanged from before transmission? Non-repuditation: The sender cannot deny sending or transmitting the data. Data encryption doesn't prevent attackers from gaining entry to your network or systems, but it does ensure that your data cannot be read or interpreted even if it's accessed by a malicious actor. 50 Data Encryption Algorithm Methods & Techniques for Effective Data Encryption Let's take a look at some of the most well-known and commonly used data encryption algorithm methods and techniques, as well as some common hash functions. They're grouped by the type of algorithm and listed alphabetically within each category. Asymmetric Data Encryption Algorithms 1. Blum–Goldwasser (BG) cryptosystem. The Blum-Goldwasser cryptosystem is a probabalistic public-key encryption scheme that was proposed back in 1984 by Manuel Blum and Shafi Goldwasser that comprises three algorithms, including a probabalistic encryption algorithm, a deterministic decryption algorithm, and a probabilistic key generation algorithm to produce a public key and a private key. This semantically-secure cryptosystem that has a consistent ciphertext expansion. As it uses a probabalistic algorithm, the BG cryptosystem can produce different ciphertexts each time a set of plaintext is encrypted. That is advantageous as cybercriminals intercepting data encrypted with the BG algorithm cannot compare it to known ciphertexts to interpret the data. 2. Boneh–Franklin scheme. The Boneh-Franklin scheme was the first practical identity-based encryption (IBE) scheme. Proposed in 2001 by Dan Boneh and Matthew K. Franklin, the Boneh-Franklin scheme is based on bilinear maps between groups, such as the Weil pairing on elliptic curves. The Private Key Generator (PKG) in the Boneh-Franklin scheme can be distributed so that to ensure that the master key is never available in a single location by using threshold cryptography techniques. 3. Cayley–Purser algorithm. The Cayley-Purser algorithm was developed by Sarah Flannery in 1999 and was inspired by Michael Purser's ideas for a Young Scientist competition in 1998. The algorithm is named after Purser and the mathematician who invented matrices, Arthur Cayley. Rather than modular exponentiation, the Cayley-Purser algorithm uses only modular matrix multiplication. It's about 20 times faster than RSA for a modulus consisting of 200 digits and is most other public-key algorithms for large moduli. However, it has since been discovered that data encrypted with the Cayley-Purser algorithm can be decrypted easily using knowledge of public data. 4. CEILIDH. The CEILIDH public-key cryptosystem, which is based on the ElGamal scheme and has similar security properties, was introduced by Alice Silverberg and Karl Rubin in 2003. Based on the discrete logarithm problem in algebraic torus, CEILIDH's primary advantage is its reduced key size compared to basic schemes for the same level of security. Named after Alice Silverberg's cat, this cryptosystem's name is also a Scot Gaelic word to describe a traditional Scottish gathering, 5. Cramer–Shoup cryptosystem. The Cramer–Shoup cryptosystem is an extension of the ElGamal scheme developed by Ronald Cramer and Victor Shoup in 1998. It incorporates additional elements compared to ElGamal to ensure non-malleability and was the first scheme proven to be effective at securing against chosen-ciphertext attack (CCA) in the standard model. 6. Crypto-PAn. Crypto-PAn (Cryptography-based Prefix-preserving Anonymization) is a type of format-preserving encryption that's used to anonymize IP addresses while preserving the structure of their subnets. It was invented in 2002 by Jinliang Fan, Jun Xu, Mostafa H. Ammar from Georgia Tech, along with Sue B. Moon and was inspired by Greg Minshall's TCPdpriv program in 1996, which adopted IP anonymization. Crypto-PAn has been found to be vulnerable to fingerprinting and injection attacks. 7. Diffie-Hellman. The Diffie-Hellman algorithm, developed by Whitfield Diffie and Martin Hellman in 1976, was one of the first to introduce the idea of asymmetric encryption. The general concept of communication over an insecure channel was introduced by Ralph Merkle in an undergraduate class project called Ralph's Puzzles, which is now deemed to be one of the earliest examples of public key cryptography. Also known as the Diffie-Hellman key exchange, it's a mathematical method that enables two unfamiliar parties to exchange cryptographic keys over a public channel securely. While it's a non-authenticated key-agreement protocol, it serves as the basis for numerous authenticated protocols. 8. El Gamal. The El Gamal encryption algorithm, based on the Diffie-Hellman key exchange, was developed by Taher Elgamal in 1985. The security strength of this algorithm is based on the difficulty of solving discrete logarithms. One downside is that the ciphertext generated by El Gamal is two times the length of the plaintext. However, it creates a different ciphertext each time the same plaintext is encrypted.

Organizations use various types of data security controls, along with their corresponding implementation methods, to safeguard their digital assets. This article delves into the main types of data security controls, their associated technologies, and how to implement them for maximum impact.

Read up on TikTok CEO Zi Chew’s largely unsuccessful testimony, a concerning series of stealthy Chinese hacks, the arrest of a malware creator, and more in this week’s Friday Five!

There's no shortage of data that can be classified. To help you build an effective data classification policy, we look at 50 different examples of data classification.

Hackers are taking advantage of SVB’s failure, scammers are leveraging psychological manipulation to empty victims’ crypto wallets, and TikTok may be in jeopardy in the U.S. Catch up on these stories and more in this week’s Friday Five!

A recently approved rule on cyber incident notification will impact how credit unions communicate reportable incidents like data breaches.

New regulations are rolling out following the release of President Biden’s National Cybersecurity Strategy, but dangers still lurk. Catch up on all the changes in this week’s Friday Five!

Although data leakage doesn’t pose the same danger as data breaches, it can still threaten organizations. Since any unauthorized transmission of data is a security violation, it is imperative organizations protect their data with data protection software like Data Loss Prevention (DLP). What Is Data Leakage? Data leakage is when data or information is accidentally exposed, disclosed, or divulged to those without authorization to access it. As opposed to data breaches that occur due to compromise from an external source, data leakage originates internally. Unlike data breaches, data leakage isn’t always due to nefarious intent. For example, it can occur in machine learning algorithms while developing predictive models. While data leakages occur accidentally or due to carelessness, they are viewed as a security flaw or violation. This is because the area from which data escapes is typically a secured network perimeter, which ought to have the wherewithal to prevent it in the first place. However, criminals can take advantage of a data leak by exploiting it to launch more pernicious, larger-scale attacks. So, while a data leakage might have innocuous origins, its impact can be devastating in the form of identity theft, ransomware propagation, and providing a pathway to data breaches. What Are the Causes of a Data Leak? Data leaks are due to various reasons, such as the following: Poor data security Without employing standard security best practices, an organization increases its chances of experiencing data leakage. They include not properly vetting third-party applications, which can expose the company to supply-chain attacks. Recycled Passwords The underlying root cause of recycled passwords are organizations that maintain poor password policies. This is also facilitated by the fact that users have to juggle an array of apps in this digital age. Left to their own devices, users reuse the same password for multiple accounts they have to log into, including corporate ones. This increases the possibility of a data leak that exposes these passwords. Hackers and malicious actors can leverage this to launch credential-stuffing attacks to compromise several corporate accounts. Misconfiguration and Poor Infrastructure Misconfigurations are one of the leading causes of data breaches. There are myriad ways misconfigurations can manifest. These improper configurations include poor settings such as using default factory configurations, shoddy permissions, inappropriate settings, and exposing secrets through a lack of proper authentication around cloud storage devices. Unpatched Software and Apps When an organization is negligent in applying security patches and updates to its software in a timely manner, it can create opportunities for data leaks and other types of vulnerabilities. Unpatched software, for instance, can open the door to a zero-day attack. Lost and Misplaced Devices Both company-issued and employee-owned devices can contain an organization’s intellectual property and corporate secrets. The loss of these devices due to theft or carelessness qualifies as data leakage that can easily escalate into a data breach. How can these types of leakages be prevented? Fortunately for organizations, several cost-effective and optimal solutions can be used to prevent data leaks. Conducting Vulnerability Assessments An organization should embrace a policy of conducting periodic vulnerability audits and threat assessments. These can be in the form of penetration tests in which the organization’s security infrastructure is probed for flaws and weaknesses. This proactive measure enables an organization to discover and safeguard potential sources of data leaks. Enhancing Document Security When data leakage occurs, it is invariably through the contents of documents that weren’t sufficiently protected. Organizations should adopt document security measures to protect their business information and corporate secrets. Control Access to Data Rampant and indiscriminate access to data increases the possibility of data leakage. To fix this, organizations should ensure that data access is tightened to only required users and apps. Organizations can achieve this by implementing robust user and cloud-based access control mechanisms and following the principle of least privilege (PoLP). Evaluate and Prevent Third-party Risks An organization might apply the requisite security practices and due diligence but can be exposed to vulnerabilities in its third-party applications. Organizations should monitor third-party applications, including open source and other supply-chain applications, to prevent becoming compromised. Implementing Robust Endpoint SecurityWith the proliferation of remote work, mobile phones, and bring-your-own devices (BYOD) in workplaces, endpoints have become crucial points of data leaks. As a result, companies should strengthen endpoint security by applying multi-factor authentication and intrusion detection mechanisms. Implementing Zero-Trust Security The rise of cloud-based computing, coupled with the explosion of endpoints, including mobile devices, means that for many organizations, perimeter-based security no longer suffices. As a result, cybersecurity practices can no longer afford to trust users and applications already inside the network. Instead, companies should adopt zero-trust security and its mantra of “never trust, always verify.” Implementing Data Loss Prevention (DLP) Tools Data loss prevention is akin to killing two birds with one stone, as it protects and defends against both data leaks and data breaches. Here are the following ways DLP can help: Providing overarching visibility: DLP can provide the high-level and granular visibility necessary to combat data leakage. Infosec teams and network administrators can effectively monitor the network, especially in large organizations. Data leak prevention: DLP software has built-in anomaly detection mechanisms. Most of these are now boosted by artificial intelligence to detect and flag suspicious transfers and movement of data to stop illegal exfiltration. Securing data at all stages of the data lifecycle: DLP solutions can secure data, whether at rest, in motion, or in use. It achieves this by combining data security policies with encryption mechanisms. Data identification: First, data categorization techniques can help a business determine whether data needs to be protected. Moreover, based on this identification, it assists in prioritizing risk, which guides the level of protection to be applied. Securing endpoints: Endpoint DLP is specifically designed to safeguard and overcome the challenges of protecting corporate endpoints like IoT and mobile devices. How Digital Guardian Secure Collaboration Can Help You Stop Data Leakage When paired with DLP, Digital Guardian Secure Collaboration can help tighten up your data protection strategy and protect your data anywhere, wherever it travels. Digital Guardian Secure Collaboration is also highly flexible, allowing you to nimbly apply policies to manage and audit data in real-time. To learn more about how Digital Guardian Secure Collaboration can secure your data and how Digital Guardian Secure Collaboration works alongside DLP solutions like Fortra’s Digital Guardian, click here.

In this week’s Friday Five, catch up on the latest warnings from CISA, the Biden Administration’s new cybersecurity strategy, the actions of Chinese-backed hackers, and much more.

In this digital era, data has become the most important currency around which e-commerce and business revolve. Data-centric security highlights this by providing greater emphasis on the data itself rather than the technologies, and infrastructure, surrounding it. What Is Data-Centric Security? Data-centric security revolves around the actual data, focusing on core attributes like its lifecycle and dependability rather than the risks associated with inadequate security infrastructure protecting it. As a result, it involves protecting data wherever it is, whether at rest, in motion, or in use. This makes sense since most of the data an organization generates rarely stays within the confines of its corporate network. Instead, it is shared with third parties, advertisers, and other outside collaborators. Data-centric security represents a paradigm shift from the traditional security route organizations follow to protect data, which mainly consists of beefing up their digital infrastructure. While technology is still involved in data-centric security, its solutions are more geared towards providing layers of governance, policies, and best practices to protect data. This focus on data extends to how it is stored, where it is located, and how it is accessed. What Are The Advantages of Data-Centric Security? As data becomes increasingly valuable as a competitive advantage, organizations have increased spending on their cybersecurity apparatus. Yet, this hasn’t truly mitigated cyber attacks, hacking, and other security breaches from occurring. Lowering the Compliance Cost of Data By focusing on the data itself, data-centric security ultimately reduces the incidence of data security breaches. It also lowers the cost of maintaining compliance, often requiring constantly updating equipment, systems, and their underlying technology. Improved Handling of a Remote Workforce Technological changes and the Covid pandemic accelerated the adoption of a remote workforce. However, the proliferation of remote endpoints outside corporate infrastructure and networks drastically increased security risks posed to data. Adopting a data-centric solution that protects data wherever it goes reduces the risks highlighted by remote work. Guaranteeing File-Level Security Data-centric security involves more than a pivot from the traditional infrastructure-focused approach. It applies more granularity to data security by leaning more heavily on file-level security. This, in turn, makes it easier to track, store, and safeguard your data. In addition, file-level security facilitates the implementation of robust encryption mechanisms, along with strong access controls and policy enforcement. Without this document security, you can more reliably control what and when users can access resources. Creating Data Security Independent of Device or System Data-centric security relieves organizations of the burden of being beholden to any system or device. By building strong cybersecurity regardless of platform, they have more leeway for data management, especially with their supply chains. This is vital because while security infrastructure can fortify a system, it often results in presenting or providing security as an end in itself instead of the means to an end – which is protecting an organization’s data crown jewels. Moreover, data security independent of a system mitigates the risk or possibility of an attack on the organization’s data. Data-centric solutions also reduce the incidences of data silos and harm when a systemic failure occurs. How to Create a Data-Centric Security Model Creating a genuine data-centric security model brings security down to the data level. Defense-in-depth Defense-in-depth is the most salient feature of a data-centric security model. It entails adopting a military strategy that encloses data in successive layers of security. These concentric rings of security may start with the desktop as the outer layer, then move to network access and operating system controls before presenting authentication. Defense-in-depth provides sufficient redundancies that act as barricades of increasing complexity from one layer of security to the next. Data Discovery, Identification, and Classification The first step in building a meaningful data-centric model is auditing and taking inventory of your organization’s data across its intranet, databases, cloud systems, and various platforms. Before an organization can keep its data secure, it needs to know where it is located and how it is stored. The next step is properly classifying and labeling the data because you cannot accurately deploy protection until you know the value of the data you are dealing with. Once data classification has been achieved, possibly with the means of automation, infosec teams can prioritize the level of protection each category of data deserves. For instance, intellectual property information like patents and company secrets might need to be protected differently from, say, credit card details. Identity and Access Management (IAM) Identity and access management is a critical part of data-centric security. IAM ensures that only authorized users can access an organization’s data. Coupled with the principle of least privilege (PoLP), it provides the necessary controls so that users are exposed to only the data required to perform their duties. Governance and Compliance To be truly effective, data-centric security must adhere to industry-specific and governmental regulations, including federal and international mandates. One of the most all-encompassing is the General Data Protection Regulation (GDPR) of the European Union (EU). If your organization operates in the healthcare industry, then HIPAA laws cover the storage, handling, and overall confidentiality of patient information. As a result of data regulations, organizations adopting data-centric solutions must periodically conduct risk management audits to ensure they are maintaining compliance with data governance rules. Data Loss Prevention (DLP) One of the best ways to approach data-centric security is to incorporate a data loss prevention solution. DLP excels in preventing data from entering into the wrong hands or being exposed to unauthorized access. It detects and prevents data loss from data breaches, data leakages, and data exfiltration. DLP uses encryption and data masking to obfuscate and protect the data from unauthorized access and illegal tampering. How can Digital Guardian Secure Collaboration help you with Data-Centric Security? Digital Guardian Secure Collaboration possesses the correct tools to aid organizations in their data-centric security journey. With secure file collaboration technology, like digital rights management (DRM) and information rights management (IRM), Digital Guardian Secure Collaboration can help complement your DLP solution and extend your data protection strategy across your enterprise. Digital Guardian Secure Collaboration solutions provide data security that travels with your digital crown jewels wherever they go. Moreover, our solution works independently of the platforms, applications, and databases you use. To learn more about data loss prevention and how we integrate with DLP solutions, like Digital Guardian, here.

The dark web isn’t what it once was with Hydra, but cybercriminals are staying busy with ransomware, targeted attacks against developers, and more. Catch up on all these stories and more in this week’s Friday Five!