Tag: data privacy

Secure-by-Design Technology

Posted on by Nicole Tidwell

While the need for total digital security has only increased over the past decades, the technology we rely on every day is often far from as secure as consumers assume. While virtually all devices, networks, and users utilize some form of information security practices, the overwhelming majority of these are separate systems that aim to keep outsiders from accessing vulnerable networks and data stores rather than improvements to the intrinsic security of the technology. 

While this may seem sufficient for some cases, the reality is that most security solutions are woefully inadequate when it comes to addressing the inherent flaws and vulnerabilities of cybersecurity technology. 

This issue has not escaped the notice of major regulatory agencies either. Earlier this year, Jen Easterly, director of the US Cybersecurity and Infrastructure Security Agency (CISA), criticized tech companies for their failure to prioritize the safety and privacy of consumers. This indictment is particularly potent coming from Easterly, who heads the United States’ national effort to understand, manage, and reduce risk to digital and physical infrastructure. 

The Burden of Safety

In many critical industries, a combination of legislation and presumed ethical responsibility mandate designers and manufacturers to account for the safe, secure usage of all new products from the outset. The world of technology, however, lacks many of these safeguards. 

The reasons for this are manifold. For one, the tech industry, as we currently know it, is still relatively young. For example, it was more than 80 years from the time automobiles were introduced until the US federal government mandated that all new cars being sold must have built-in seatbelts. 

Another reason that new technology pertaining to the cybersecurity space often lacks the oversight present in other industries relates to the nature of the threats in question. While the potential for accidental user-caused data breaches certainly exists to some extent, the majority of modern data threats come from malicious actors. This is the current industry dynamics that make it easier for tech companies to pass off the burden of safety, making it the responsibility of customers to protect themselves from attackers. 

While it is still up for debate on whether or not tech companies should be held responsible for the safety of their products, CISA Director Easterly was clear in her Carnegie Mellon University talk on where her organization stands regarding where the burden of security lies. 

“We find ourselves blaming the user for unsafe technology. In place of building-in effective security from the start, technology manufacturers are using us, the users, as their crash test dummies — and we’re feeling the effects of those crashes every day with real-world consequences,” she said. “This situation is not sustainable. We need a new model.” 

Information Security Legislation

Despite the lack of regulation surrounding the creation and distribution of software and Data-Centric technologies, the information stored and transferred using these tools is often bound by strict legislation. For instance, in the United States, all information related to individual health is protected under the Health Insurance Portability and Accountability Act of 1996 (HIPAA). Compliance with HIPAA regulations is dictated by the US Department of Health and Human Services and enforced by the Office for Civil Rights. 

Moreover, it should also be noted that non-compliance with privacy laws such as HIPAA for health-related data, CCPA legislation in California, or the GDPR (pertaining to EU subjects) is prone to penalization. 

Secure-by-Design Technology

Critical security concerns surrounding data that relies on digital privacy measures highlight the need for a better data protection paradigm than most individuals and organizations currently use. This is where “secure-by-design” technology is urgently needed. 

In the current system, tech companies create and sell technology that leaves users to contend with suboptimal solutions to their own security needs. However, as the name suggests, secure-by-design technology is created with privacy and security and embedded into a data-file from its origination to its expiration. 

CISA Director Easterly noted the importance of this approach in her address, pointing out that “… ultimately, such a transition to secure-by-default and secure-by-design products will help both organizations and technology providers: it will mean less time fixing problems, more time focusing on innovation and growth, and importantly, it will make life much harder for our adversaries.”

For now, the vast majority of ubiquitous security solutions are simply bandages over the inherent flaws of digital networks. However, a better, more fundamental type of cybersecurity does exist. 

Self-Protecting Data and Zero-Trust Security

Whether or not new regulations will compel the technology industry to create fundamentally more secure systems in the future, sensitive data — currently stored in digital spaces — already faces more threats than ever before. 

To date, the concept of perimeter security has been the de facto standard for data security. With the advent of the internet, securing networks has become a greater priority, and reliance on tools such as IP address verification and multi-factor authentication has only increased. Although relatively mature, these methods still serve as the primary ways in which most companies attempt to ensure that private information stays private. 

While perimeter security continues to serve an important purpose in protecting secure files, this form of traditional data protection is fundamentally flawed. When an organization’s defense relies purely on perimeter security, identifying and addressing vulnerabilities becomes a game of whack-a-mole between hackers and network administrators. 

Both conceptually and in practice, Zero-Trust security is a revolution. Rather than rely on a series of firewalls and trust that those with access are legitimately allowed to be there, Zero-Trust security protects data by demanding continuous authentication from users. Meanwhile, self-protecting data protocols — unlike perimeter security — are designed to give data files the ability to protect themselves from creation. 

Sertainty

As a leader in self-protecting data, Sertainty leverages proprietary processes that enable data to govern, track, and defend itself. These protocols mean that even if systems are compromised or accessed from the inside, all data stored in them remains secure. 

At Sertainty, we know that the ability to maintain secure files is the most valuable asset to your organization’s continued success. Our industry-leading Data Privacy Platform has pioneered what it means for data to be intelligent and actionable, helping companies move forward with a proven and sustainable approach to their cybersecurity needs. 

As the digital landscape evolves and networks become more widely accessible, Sertainty is committed to providing self-protecting data solutions that evolve and grow to defend sensitive data. Open-source security breaches may be inevitable, but with Sertainty, privacy loss doesn’t have to be. 

AI Optimization and Anonymization

Posted on by Nicole Tidwell

Today, artificial intelligence is no longer the far-off dream it once was. Tools like Midjourney, ChatGPT, and others have taken off in the last year, bringing with them a barrage of questions.  Many cybersecurity experts, and those entrusted with handling sensitive information, have pegged data privacy as the likeliest potential threat that these programs pose to organizations. 

The capabilities of AI are surmounting daily. Cybersecurity risks are mounting in step. From the first moment an AI Engine is optimized, it starts processing datasets. Partly because of this, effective data anonymization has become critical due to various compliance regimes and consumer protection laws. Companies hoping to utilize the power of artificial intelligence must factor in which datasets, audiences, and business problems it seeks to ascertain their predictions. 

What Is AI Optimization? 

Before testing an AI program, it must be optimized for its intended application. While, by definition, these programs are always learning, the initial training and optimization stage – which is defined by Volume, Variety, and Variance, is an essential step in the AI development process. 

There are two modes of AI training: supervised and unsupervised. The main difference is that the former uses labeled data to help predict outcomes, while the latter does not. 

The amount of data available to AI dictates whether developers can extract inputs to generate a significant and nuanced prediction in a controlled environment. Depending on data accuracy, developers will intervene and recast an existing outcome into a general output and reiterate the unsupervised processing w for better quality control and outcome. 

Supervised Learning

In this context, labeled data refers to data points that have been given pre-assigned values or parameters by a human. These human-created points are then used as references by the algorithm to refine and validate its conclusions. Datasets are designed to train or “supervise” algorithms to classify data or predict outcomes accurately. 

Unsupervised Learning

While no machine learning can accurately occur without any human oversight, unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns in data without the need for human intervention, making them “unsupervised.” 

While more independent than supervised learning, unsupervised learning still requires some human intervention. This comes in the form of validating output variables and interpreting factors that the machine would not be able to recognize. 

Data Anonymization in Machine Learning

The majority of machine learning advances of the past three decades have been made by continuously refining programs and algorithms by providing them with huge volumes of data to train on. ChatGPT, one of the most popular AI platforms today, is an open-source chatbot that learns by trolling through massive amounts of information from the internet. 

For all of their impressive capabilities, however, AI programs like ChatGPT collect data indiscriminately. While this means that the programs can learn very quickly and provide comprehensively detailed information, they do not fundamentally regard personal or private information as off-limits. For example, family connections, vital information, location, and other personal data points are all perceived by AIs as potential sources of valuable information. 

These concerns are not exclusive to ChatGPT or any other specific program. The ingestion of large volumes of data by AI engines magnifies the need to protect sensitive data. 

Likewise, in supervised machine learning environments, anonymization for any labeled data points containing personal identifiable information (PII) is key. Aside from general concerns, many AI platforms are bound by privacy laws such as HIPAA for health-related data, CCPA legislation in California, or the GDPR for any data in the EU. 

Failing to protect the anonymity of data impacted by these laws can result in steep legal and financial penalties, making it crucial that anonymization is properly implemented in the realm of AI and Machine Learning. 

Pseudonymization vs. Anonymization

When discussing data privacy, the word anonymization is almost always used, but in reality, there are two ways of separating validated data points from any associated PII. In many cases, rather than completely anonymizing all data files individually, PII is replaced with non-identifiable tags (in essence, pseudonyms). 

Perhaps the most famous large-scale example of this is blockchain technology. While personal data such as real names or other PII are not used, in order for the record-keeping chain to function, all data for each user must be linked under the same pseudonym. While some people consider this to be sufficiently anonymous for their purposes, it’s not as secure as true anonymization. If a pseudonym is compromised for any reason, all associated data is essentially free for the taking. 

True anonymization, on the other hand, disassociates all identifying information from files, meaning that the individual points cannot be linked to each other, let alone to a particular person or parent file. 

Because of this, many security experts prefer to avoid the half-measure of pseudonymization whenever possible. Even if pseudonymous users are not exposed by error or doxxing, pseudonymized data is still vulnerable in ways that fully anonymized data is not. 

Already, some AIs are becoming so sophisticated that they may be able to deduce identities from the patterns within pseudonymized datasets, suggesting that this practice is not a secure replacement for thorough anonymization. The more data algorithms are trained on, the better they get at detecting patterns and identifying digital “fingerprints.” 

Other AI-Driven Anonymization Scenarios

In the current landscape of ever-more-capable machine learning, the value of proper data anonymization is greater than ever. Aside from the vulnerabilities within AI-driven frameworks, external threats driven by digital intelligence present new challenges, as well. 

For one thing, artificial intelligence is able to exploit technical loopholes more effectively than human hackers. But beyond that, AI is also increasing threats targeted at social engineering. Recently, users found that ChatGPT was able to generate phishing emails that were notably more convincing than many human-generated attempts. This will undoubtedly lead to increasingly sophisticated attempts to access private data. As such, new tactics must be employed to properly secure and anonymize data before it becomes exposed to artificial intelligence.

Anonymized Smart Data with Sertainty

Sertainty’s core UXP Technology enables Data as a Self-Protecting Endpoint that ensures the wishes of its owner are enforced. Sertainty’s core UXP Technology will also enable developers working within AI environments such as ChatGPT to maintain ethical and legal privacy with self-protecting data. Rather than attempting to hide PII and other sensitive data behind firewalls, Sertainty Self-Protecting Data files are empowered to recognize and thwart attacks, even from the inside. 

As a leader in self-protecting data, Sertainty leverages proprietary processes that enable data to govern, track, and defend itself in today’s digital world. These protocols mean that if systems are externally compromised or even accessed from the inside, all data stored in them remains secure. 

At Sertainty, we know that the ability to maintain secure files is the most valuable asset to your organization’s continued success. Our industry-leading Data Privacy Platform has pioneered what it means for data to be intelligent and actionable, helping companies move forward with a proven and sustainable approach to their cybersecurity needs. 

As the digital landscape evolves and networks become more widely accessible, Sertainty is committed to providing self-protecting data solutions that evolve and grow to defend sensitive data. With the proliferation of human and AI threats, security breaches may be inevitable, but with Sertainty, privacy loss doesn’t have to be.

How Self-Protecting Data Creates Truly Secure Files

Posted on by Nicole Tidwell

Technology has taken leaps and bounds forward in the last few decades. This growth has expanded our capabilities and access to computing power. As data applications have become more widespread and versatile, our reliance on secure files has also increased. 

Cybercrime has been quick to interject itself with the exponential growth of unstructured data files. Network computing today, whilst truly innovative, is replete with major attacks aimed at shutting it down. The motivation behind these breaches has ranged from simple thievery and greed to catastrophic acts of global cyberterrorism. Moreover, the Dark Web continues to be populated with tools and malware that make this onslaught continuous and dire. 

As much as both private companies and government agencies work to secure files and networks, hackers are never far behind. Often, the tools that make sensitive networks so accessible and valuable are also their Achilles heels. 

The Limits of Traditional Security

The vast majority of the most complex security systems operate on the same basic principle: to keep malicious actors or programs out of your secure files. Marketing claims notwithstanding, most of these systems approach cyber security issues with a similar method, almost invariably using some form of perimeter security. 

To date, the concept of perimeter security has been the de facto standard for data security, even predating the firewall. Even the earliest computers that operated on closed networks kept themselves secure by restricting who could use the computer terminal. This then advanced to dedicated user accounts and passwords. With the advent of the internet, securing networks became an even greater priority. Reliance on tools such as an IP address and verification and multi-factor authentication serve as the primary ways to ensure that private information stays private. 

While perimeter security continues to serve an important purpose in protecting secure files, this form of traditional data protection is fundamentally flawed. When an organization’s defense relies purely on perimeter security, identifying and addressing vulnerabilities becomes a game of whack-a-mole between hackers and network administrators. 

Irrespective of how good your administrators are, ways into a system will always exist. Once a private system’s perimeter has been breached, users can do as they please. This means that not only are compromised credentials a threat, but conventional perimeter security systems are exceedingly vulnerable to inside attacks. 

How Does Self-Protecting Data Work?

Rather than simply trying to improve on inherently flawed concepts, self-protecting data is the result of rethinking our security fabric. As the name implies, the goal of self-protecting data is not simply to keep hackers out of your system but to create truly secure files. 

While the mechanisms of self-protecting data are extremely intricate, the fundamental concept is fairly straightforward. Instead of being left accessible to “approved” users, the files themselves are coded with the ability to recognize malicious activity and counter it immediately, regardless of who performed the action. 

Operating on a Zero-Trust basis connotes that basic perimeter security like password-protected logins becomes a first layer of defense rather than the sole source of protection for your files. Enhancing your defenses with the Sertainty Self-Protecting-Data (SPD) not only stops an outside actor who has infiltrated the system from wreaking havoc, but it also prevents insiders from creating chaos. 

Types of Threats to Secure Files

To better understand how SPD creates truly secure files, we must consider what attackers are attempting to accomplish. Let’s take a look at some types of attacks and see how SPD identifies and negates \ mitigates them. 

Ransomware

In ransomware attacks, hackers will create a program that has the ability to block access to secure files or a system, usually threatening to delete data if an organization does not comply with a specific set of demands. In a conventional security system, a user or program that has gained the ability to execute code within your network has the power to deploy malware in a system to exact ransomware. 

SPD files, however, are given the ability to recognize when a malicious program is attempting to gain control over it and block access to it whilst alerting system admins by themselves. Not only does this prevent the ransomware from harming secured files, but it can also provide valuable metadata about the attempt, giving insights needed to strengthen an organization’s security system further and factor continuity of operations to maintain resiliency. 

Social Engineering

Unlike “direct attacks,” where malicious programs are created to exploit a specific weakness in a security system, social engineering attacks attempt to trick employees or other legitimate users into compromising their credentials. These can come in the form of phishing emails or phone calls, malicious links, key tracking software, and other forms of trickery. 

Once they have captured the appropriate login credentials, hackers are free to do as they please within your system until you catch them and lock them out again. Because Sertainty SPD embeds a Zero-Trust framework within files, malicious actions are blocked and reported, even if they’re taken by a party with valid credentials but out of context and geographical location.  

Insider Attacks

Because insider attacks come from parties who already have legitimate access to a system, any form of perimeter security is, by definition, useless. But with the Sertainty SPD, even fully legitimate and “trusted” members of your organization are defended against by the files themselves. This not only prevents rogue parties from stealing or destroying valuable data, but it also protects against accidental actions that can harm your secure files. 

Truly Secure Data with Sertainty

As a leader in self-protecting data, Sertainty leverages proprietary processes that enable data to govern, track, and defend itself. These protocols mean that even if systems are compromised or accessed from the inside, all data stored in them remains secure. 

At Sertainty, we know that the ability to maintain secure files is the most valuable asset to your organization’s continued success. Our industry-leading Data Privacy Platform has pioneered what it means for data to be intelligent and actionable, helping companies move forward with a proven and sustainable approach to their cybersecurity needs. 

As the digital landscape evolves and networks become more widely accessible, Sertainty is committed to providing self-protecting data solutions that evolve and grow to defend sensitive data. Open-source security breaches may be inevitable, but with Sertainty, privacy loss doesn’t have to be. 

Is Blockchain Really as Secure as it Seems?

Posted on by Nicole Tidwell

For nearly a decade and a half, cryptocurrency and the blockchain technology that powers it have played an increasingly central role in cybersecurity and online privacy discussions. Bitcoin and other cryptocurrencies have been touted as truly anonymous ways of storing and spending money, and popular perception remains, which is that blockchain itself is “unhackable.” 

While the idea of digital currency or decentralized data is not a new one, functioning blockchains are still relatively new. The technology became viable in 2008 when a person (or group of people) using the name ‘Satoshi Nakamoto’ introduced the first digital currency that addressed decentralization’s past issues by creating the first viable blockchain. Since then, various applications for blockchain technology have been developed, mostly due to its inherently incorruptible nature. 

How Does the Blockchain Work? 

Sometimes referred to as distributed ledger technology, a blockchain is a type of online database that maintains records in the form of “blocks” of information that are cataloged in chronological order. This creates a “chain” of data blocks, each representing an event in the history of the complete system. Each time a new transaction is completed, a new block is added, continuing the ledger of information. 

Blockchains come in two primary forms, public and private. In public chains, users from anywhere can join, becoming a part of the chain of nodes, sending and receiving transfers of data and currency that are then included in the chain. On the other hand,  private chains only allow users that have been granted permission to access transaction data. Both private and public chains can also be “permissionless” or “permission restricted,” depending on whether or not users within the network have the ability to validate transactions or merely utilize the existing nodes. 

It’s worth noting that blockchain technology can be used to send, receive, and track where files are sent. However, the actual data within the blocks remain private. The data itself is only accessible to the user(s) with the correct digital ‘keys.’ The databases where information shared using a blockchain is stored still have the same features and vulnerabilities, regardless of how securely that data may be shared.

A Reputation for Inherent Security

As we mentioned earlier, a common perception among those who use any form of blockchain technology is that this type of system is impenetrable. Like conventional digital ledgers, the record of events is intended to be permanent, with each block becoming unchangeable once it’s accepted into the chain. However, unlike traditional systems, blockchain data is stored across multiple nodes hosted in different locations. The wider the web of nodes spreads, the more fail-safes the system has. 

The result is a theoretically corruption-proof system. In theory, if a secure node (or nodes) were to be compromised, the rest of the blockchain would recognize the discrepancies and prevent false information from being accepted. 

Blockchain’s Limitations

While all of this makes large blockchains fundamentally more reliable than single-source records, no system is completely immune to threats. The dangers to the blockchain can come from users within a network or outside of it. These dangers must be considered before you put all of your faith into a system on reputation alone. 

51% and Sybil-Type Attacks

While the record of shared information is protected by the wide variety of verification data centers in the system, malicious actors can target the network itself. The two most obvious threats to blockchain networks come in for form of “51%” attacks and “Sybil-Type” attacks. 

During 51% of attacks, hackers attempt to generate enough data verification nodes to outnumber the number of legitimate nodes. If a single party can gain control of more than half of a blockchain’s nodes (hence the name), the information they present will be seen by the system as the ‘real’ record, and the previously existing, legitimate chain will be overruled.

Additionally, 51% of these attacks are only practical in smaller networks. Major blockchains, like Bitcoin, are far too vast for any one group to take control. Additionally, these attacks can be mitigated using a permission-restricted system so only verified users can create new nodes. 

Sybil-type attacks, so-called after a book of the same title, refer to an attack by users who attempt to create an overwhelming number of false transactions with false identities. These attacks flood the chain with unreliable information and overwhelm the system. Sybil-type attacks share some similarities with other blockchain threats, but they are easier to create in public chains. These attacks can be prevented if there is a high cost to create new accounts to discourage users from creating enough to disrupt the chain. 

Compromised User Accounts and Routing Attacks

Like with many digital systems, the greatest vulnerabilities of all come from the human component. While correctly moderated blockchains may be extremely resistant to intervention, users in the system are always vulnerable to phishing, RAT attacks, and other social engineering scams that jeopardize credentials and digital keys. 

Due to the impact of human error, data shared via the blockchain can be verified as coming from a legitimate source; however, there’s no guarantee of safety once it has reached its destination. Crypto wallets, private databases, and more can all still be breached by inside or outside actors.

Cryptocurrency Exchange Trustworthiness

If sending money over blockchain, users need to familiarize themselves with the crypto exchange. Although many tout the safety and security of the blockchain, using cryptocurrency for transactions isn’t safe as what was once alluded to. With the recent collapse of FTX and loss of $2 billion in user funds, businesses and individuals alike could be at the mercy of how these private organizations are handling both data and money. 

Truly Secure Data with Sertainty 

Regardless of the enhanced legitimacy of decentralized ledger systems, data breaches remain a significant concern for any conventionally-protected network. Utilizing a public or private blockchain can be one part of your data protection strategy. However, to guarantee that network breaches don’t leave you vulnerable, you must ensure that your data files are truly secure. 

Rather than rely on a series of firewalls and trust that those with access are legitimately allowed to be there, Zero Trust security gives data the ability to protect itself. Following this methodology, Sertainty has redefined how information is protected to ensure data privacy even where firewalls fail. Using cutting-edge protocols and embedding intelligence directly into datasets, Sertainty leverages proprietary processes that enable data to govern, track, and defend itself. These protocols mean that even if systems are compromised, data remains secure. 

As the digital landscape evolves and networks become more widely accessible, Sertainty is committed to providing self-protecting data solutions that evolve and grow to defend sensitive data. Instead of focusing on your network’s inherent shortcomings, we enable our partners to safely and confidently embrace the potential of a new online-oriented world. Data breaches may be inevitable, but with Sertainty, privacy loss doesn’t have to be.

How “Bring Your Own Device” Policies Are Feeding the Rise of Mobile Threats

Posted on by Nicole Tidwell

Driven by the shift to remote and hybrid work models, more and more people are using their personal devices for work purposes. A vast majority of Americans own smartphones, and many use those phones to access internal company documents and databases. But while this may be a convenient habit, it also introduces complex security risks.

Sensitive data is at a greater risk than ever before, with high-profile breaches making headlines. Understanding the threats to workers’ personal IT assets is vital in today’s connected landscape. As the proliferation of devices opens up potential network vulnerabilities, innovative security has to stay one step ahead of evolving digital threats. 

The Shift to BYOD 

Over the last decade, companies have been moving toward “Bring Your Own Device” policies, encouraging employees to use their own devices for work tasks. The onset of COVID-19 and the subsequent shift towards remote working has only increased this trend. But why are employers so quick to embrace this approach?

In addition to lower equipment costs for companies, BYOD means that employees can spend less time training to use new systems and harness the increased productivity of more familiar devices. BYOD also involves less accountability for managing IT assets, which workers can take to and from home at will. But for all of the conveniences and seeming efficiency, adding unmonitored devices that may have varying levels of security measures presents numerous opportunities for data breaches. 

Growing Mobile Use Statistics

Mobile devices are more ubiquitous than ever before for both professional and personal use. According to Statista, more than 91% of the global population (7.26 billion people) owns a mobile phone. The agency also found that 83.4% of people own a smartphone. This is a considerable rise, up from just 49.4% in 2016. 

Now that the overwhelming majority of the world owns smartphones, people commonly use them for tasks that were previously relegated to desktops and laptops. An August 2022 study found that 41.6% of emails were opened on mobile, with desktop browser email accounting for only 16.2% of opened emails. With so much of our information being stored and exchanged on mobile devices, understanding the potential data risks is essential. 

Misconception: Mobile Operating Systems Are Less Vulnerable 

Contrary to what consumers may assume, mobile devices are no more secure than other computers. Recently, the United States Computer Emergency Readiness Team (US-CERT) issued a report highlighting the dangers present in mobile devices. The report cited the increase in threats specific to mobile phones and existing vulnerabilities in all operating systems. This report also points out that typical attacks leverage mobile devices’ portability and their similarities to PCs. The mistaken perception that mobile operating systems are fundamentally more protected is dangerous, allowing hackers to take advantage of users’ naivete to exploit holes in their device security.

The Rise in Attacks Targeting Mobile Platforms and Devices

Mobile devices have many unique features, some of which introduce unique vulnerabilities. As global smartphone users increase, so do cybersecurity dangers. Recent years have seen a number of growing threats to mobile users. Among these, one of the most prevalent threats is mobile app fraud. A prominent breach in 2020 saw hackers use a massive network of devices to drain millions of dollars from online bank accounts, and single emulators can spoof thousands of devices simultaneously. Cross-border fraud is another rising concern, with 60% of businesses in the US and UK reporting incidents of this fraud type in 2021. 

Account takeover (ATO) attacks present yet another serious data security threat. Countless data breaches have leaked user identity information over time, making it easy for malicious actors to steal credentials that open doors to sensitive information. ATO attacks are one of the fastest-rising threats currently facing organizations and consumers alike. 2021 saw a nearly 20% increase in data breaches compared to 2020. Combined with phishing, social engineering scams, and AI-assisted machine-learning hacks, compromised login credentials are creating deep concerns among data security experts. 

The Need for Truly Secure Data

Known threats are not the only danger. According to the Identity Theft Resource Center’s 2022 H1 report, approximately 40% of data breach notices issued in the first half of 2022 did not include the root cause of the compromise. The top cause of data breaches so far this year is “unknown” due to a lack of missing root cause identifiers. For the first time since the ITRC began tracking data breach causes, the majority are unknown. Patching all of the potential holes in a security perimeter is especially challenging when not all threats are easily identified. The only truly safe solution is data that protects itself at every stage and, crucially, when accessed through any gateway.

BYOD policies are opening your network to a multitude of devices, many of which you cannot track or control. And while basic security measures like employee training, firewalls, and multi-factor authentication are still essential, they lose their value as soon as a breach has occurred. That’s why it’s vital to partner these measures with self-governing data, which protects against perimeter breaches. 

Traditionally, organizational data has been hidden behind firewalls and is left vulnerable to those already inside the system. However, Sertainty has redefined how information is protected to ensure data privacy even where firewalls fail. Using cutting-edge protocols and embedding intelligence directly into datasets, Sertainty leverages proprietary processes that enable data to govern, track, and defend itself. These protocols mean that even if systems are compromised, data remains secure.

At Sertainty, we know that data is the most valuable asset to your organization’s continued success. Our industry-leading Data Privacy Platform has pioneered what it means for data to be intelligent and actionable, helping companies move forward with a proven and sustainable approach to their cybersecurity needs.

Instead of focusing on your network’s inherent shortcomings, we enable our partners to safely and confidently embrace the potential of a new online-oriented world. Data breaches may be inevitable, but with Sertainty, privacy loss doesn’t have to be.

How Sertainty Serves and Preserves the Data Chain of Custody

Posted on by Nicole Tidwell

Sertainty and the Convergence of an OT and IIoT Environment

Posted on by Nicole Tidwell