Predictive Security: Trends and Challenges

In a world where digital threats are constantly evolving, the concept of predictive security is becoming more critical than ever. Unlike traditional reactive security measures, predictive security is about staying one step ahead, anticipating cyber threats before they materialize. This new approach is revolutionizing how we think about and implement cybersecurity. 

What Is Predictive Security? 

Predictive security is a proactive approach focusing on forecasting and mitigating cyber threats before they can cause harm. It uses advanced analytics and machine learning to predict and prevent attacks. This approach contrasts reactive security measures that primarily deal with threats after they have occurred. Traditional security models, such as firewalls and antivirus software, often operate on a reactive basis, addressing vulnerabilities only as — or after — malicious actors exploit them. 

By identifying potential vulnerabilities and attack vectors, predictive security enables organizations to strengthen their defenses proactively. Such foresight is crucial in an era where cyber threats are becoming increasingly sophisticated and more challenging to detect with conventional methods. 

The Mechanics of Predictive Security

In predictive security, large datasets are instrumental in forecasting potential cyber threats. By analyzing historical data, predictive models can identify patterns and anomalies that may signify an impending attack. AI and machine learning enable the analysis of vast amounts of data at a speed and accuracy unattainable by human analysts

Integrating Predictive Security in a Defense-in-Depth Framework

Integrating predictive security into existing cybersecurity frameworks signifies a strategic and forward-thinking move. It necessitates a harmonious blend of predictive and reactive security strategies, forming a comprehensive, multi-layered defense mechanism. This approach aligns well with the defense-in-depth framework, a cybersecurity strategy that employs multiple layers of defense to protect against threats at different levels. 

By infusing predictive security into this framework, organizations can respond to imminent threats and anticipate and neutralize potential risks before they manifest. This integration usually involves revising cybersecurity policies and investing in cutting-edge technologies capable of advanced predictive analytics, reinforcing each defense layer with proactive intelligence and foresight. 

Advancements in Predictive Security Technologies

Recent technological innovations have significantly enhanced the capabilities of predictive security. Developments in AI, machine learning, and data analytics have led to more accurate and efficient prediction models. These advancements are shaping the current state of cyber defense and paving the way for future innovations in cybersecurity. 

Likewise, innovations in data-centric security have also played a pivotal role in advancing predictive security. One notable development in this area is the concept of self-protecting data. This technology embeds security directly into the data, enabling it to detect and respond to threats autonomously. Such an approach ensures that data remains protected regardless of where it is stored or how it is used, thus providing a robust, proactive, and dynamic layer of security. This concept is especially relevant in scenarios where data is highly distributed and mobile, as is increasingly the case in today’s digital environment. 

The Role of Strategic Partnerships

Strategic partnerships between cybersecurity firms and tech innovators are instrumental in driving the development and implementation of predictive security technologies. An exemplary effort in this field is the recent collaboration between data security pioneer Sertainty and GuardDog AI, a leader in AI-driven incident response.

This partnership represents a fusion of expertise in active data protection and advanced threat response mechanisms. GuardDog AI’s proficiency in AI-powered cybersecurity incident response tools and services complements the innovative Sertainty approach to data protection, which includes embedding intelligence directly into data. When combined with self-protecting data technology, it ensures that data remains secure and intelligently responsive to threats, even in complex and fast-evolving cyber environments. 

Strategic partnerships like this are vital for establishing new benchmarks in the cybersecurity field. They foster the development of comprehensive solutions that more effectively tackle both current and emerging cyber threats.

Challenges and Considerations in Predictive Security

The advent of predictive security in the cybersecurity realm marks a revolutionary step forward. However, this advancement has unique challenges and ethical considerations that organizations must meticulously navigate. 

Privacy and Data Handling

In the realm of predictive security, the management of vast quantities of personal and sensitive data presents a considerable challenge. Predictive models, which rely on extensive datasets to forecast threats, often include personal user information, thus raising critical privacy concerns. Organizations must strike a delicate balance between utilizing this data for security purposes and upholding its confidentiality and integrity. This balance necessitates stringent data protection protocols and strict adherence to privacy laws to prevent data breaches and unauthorized access. 

Similarly, the AI training models and machine learning used in these predictive models warrant close scrutiny. These powerful technologies, prone to operating as a “black box,” require transparency and accountability in their decision-making processes to prevent biases and sustain trust. Such openness is especially crucial in scenarios where automated decisions based on these technologies could have significant consequences, underscoring the need for ethical considerations in their deployment.

Data Accuracy and False Positives

The accuracy of the data used in predictive security models is another critical challenge. Inaccuracies in data can lead to false positives, where the system incorrectly identifies a benign activity as a threat. This issue is especially challenging when research shows that organizations are overwhelmingly lax in sticking to their own security protocols

Ensuring the reliability and quality of the data fed into predictive models is crucial for their effectiveness. Regular audits and updates of data sources and sophisticated data validation techniques are essential to maintain the integrity of predictive security systems. 

Adapting to Evolving Cyber Threats

Predictive security models must continually adapt to the evolving nature of cyber threats. Cybercriminals are constantly devising new methods to bypass security measures, which means predictive models can quickly become outdated. Continuous learning and adaptation are vital for these systems to remain effective. 

Such adaptation requires ongoing investment in research and development and regular updates to the predictive models to incorporate the latest threat intelligence. Organizations must also ensure that their cybersecurity personnel are adequately trained to work with predictive security systems, often requiring specialized knowledge and skills. 

The Future of Predictive Security

Integrating predictive security into cybersecurity strategies represents a crucial step forward in combating digital threats. Organizations can effectively anticipate and mitigate potential cyberattacks by leveraging advanced technologies and embracing a data-centric approach. As the landscape of cyber threats continues to evolve, the role of predictive security in shaping a more secure digital future becomes increasingly significant. 

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

At Sertainty, we understand that maintaining secure files is the most valuable asset to your organization’s continued success. Our industry-leading Data Privacy Platform has pioneered data solutions that are intelligent and actionable, helping companies move forward with a proven and sustainable approach to their cybersecurity needs. 

Security threats may be inevitable, but with Sertainty, privacy loss doesn’t have to be. 

The Future of Data Security: AI, Self-Protecting Files, and Zero-Trust

In today’s digital landscape, the future of data security is at the forefront of every organization’s concerns. With the constant evolution of cyber threats and the increasing complexity of our interconnected world, traditional security measures are no longer enough to safeguard sensitive information. 

Today, we’ll delve into the changing nature of information security threats, the limitations of conventional cybersecurity methods, and how innovative solutions like self-protecting files and zero-trust network access are shaping the future of data security. Join us on this journey as we explore the path to a more secure digital future, where organizations can protect their data with confidence.

The Evolution of Data Security

From the earliest days of computer networks, information security primarily focused on building robust perimeter defenses. Firewalls, intrusion detection systems, and access control were the standard tools in the cybersecurity arsenal. However, as technology advanced, so did the strategies of cybercriminals. The rise of sophisticated cyber threats has exposed the inadequacies of traditional security models. 

Limitations of Traditional Security Measures

The limitations of traditional security measures are evident in their inability to adapt to the evolving threat landscape. These methods often rely on static rules and predefined patterns to detect anomalies, making it challenging to detect novel attack vectors. Organizations find themselves in a constant game of catch-up, struggling to defend against new, innovative cyber threats.

Most traditional cybersecurity methods lean heavily on perimeter-based security. While firewalls and intrusion detection systems create a barrier between an organization’s internal network and the outside world, this approach has its limitations. Cybercriminals often exploit vulnerabilities to infiltrate this perimeter, making perimeter-based defenses an incomplete solution. Legacy systems and password-based authentication methods have become especially easy targets for attackers, as outdated software and weak passwords can provide cybercriminals with an open door to an organization’s sensitive data.

Insider threats pose another significant challenge. Malicious or negligent employees can bypass perimeter defenses, leading to data breaches from within.

Zero-Trust: Redefining Network Security

Zero-trust network access is a fundamental shift in the way we approach network security. Unlike traditional models that trust users and devices within the network, a zero-trust approach demands rigorous proof of legitimacy.

Zero-trust emphasizes the continuous verification and authentication of all users and devices, regardless of their location. This approach ensures that trust is never assumed, and access is granted based on real-time data and context. As a result, organizations can effectively protect their networks from both external threats and insider risks.

The Evolving Regulatory Landscape

Recognizing the need for a paradigm shift in cybersecurity, the United States government has taken significant steps to enhance data security. The Cybersecurity and Infrastructure Security Agency (CISA) has introduced the “Zero-Trust Maturity Model,” a framework designed to help organizations transition to zero-trust security. This model emphasizes continuous verification and authentication, ensuring that trust is never assumed, even within the network perimeter.

Executive Order 14028, titled “Improving the Nation’s Cybersecurity,” reinforces the government’s commitment to strengthening national cybersecurity defenses. The order highlights the importance of modernizing cybersecurity defenses and underscores the significance of implementing zero-trust principles. By aligning with government initiatives, organizations can stay ahead of cyber threats and contribute to a more secure digital landscape.

The Future of Data Security

Amid the evolving threat landscape, a revolutionary concept has emerged — self-protecting files. These files are not your typical data containers. Instead, they are intelligent, dynamic entities that possess the ability to protect themselves and the data they hold. 

Self-protecting files utilize cutting-edge technology to embed security directly into the data itself. They can determine who is accessing the data, where, when, and under what circumstances. If any aspect of the access does not align with pre-defined policies, the file can instantly revoke access or take other protective actions. 

Self-Protecting Data vs. Traditional Security

The advantages of self-protecting files over traditional security methods are profound. With self-protecting files, data protection becomes intrinsic, eliminating the need for perimeter defenses to protect data at rest. They also offer enhanced privacy and control, as data owners can define precisely how their data is accessed and used. This level of granularity in data security is a game-changer for organizations across various industries.

Other Emerging Security Technologies

Another type of emerging technology leverages advanced AI-driven algorithms to proactively identify and neutralize potential threats. They excel at detecting vulnerabilities that often evade traditional security measures, making them a vital component in safeguarding sensitive data.

One common focus of these technologies is securing the “edge territory” of networks, an often-ignored critical area where cyber criminals can exploit weaknesses. By concentrating on fortifying this network segment, these emerging solutions provide an additional layer of defense that is instrumental in today’s complex digital ecosystem.

Furthermore, these technologies are increasingly integrating with other cutting-edge security solutions, such as Sertainty’s technology and its Digital IDs. This integration not only enhances their capabilities but also fosters collaboration in creating dynamic ecosystems where data is both protected and empowered.

These pioneering approaches are setting a new industry standard for data security, coupled with a data-centric orientation. In a world where data security is paramount, these collaborations exemplify the potential of combining AI-driven security technologies to provide comprehensive protection in the digital age.

While these may seem fundamentally different than zero-trust, Sertainty technology can play an integral role in these platforms as well. For example, GuardDog AI‘s AI-powered Protective Cloud Services (PCS) platform employs cutting-edge technology to constantly scan and analyze network traffic in concert with the Sertainty software developer toolkit

This integration brings a unique fusion of technologies. Sertainty, a global data security leader, is known for its Data Privacy Platform, which empowers data files to protect themselves using a zero-trust methodology. This approach prioritizes data-centric security, ensuring privacy and integrity even in situations where traditional security measures may fall short.

Truly Secure Data with Sertainty

The future of data security lies in innovative solutions like self-protecting files and zero-trust network access. With the changing nature of cybersecurity threats and the limitations of traditional security measures, organizations must adapt to stay secure. 

Sertainty technology bridges the gap between technologies shaping the future of data security (self-protecting files and zero-trust network access) with a software development kit that can be seamlessly integrated into a wide range of applications. As we navigate the digital future, the path to a more secure data environment becomes clear — a path paved with innovation, adaptability, and trust in the face of evolving threats. 

Explore Sertainty’s solutions and embark on this journey towards a safer digital world.

Secure-by-Design Technology

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. 


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. 

Addressing Primary Open-Source Security Challenges

In the modern era of computing and data storage, the most critical element of any system is the software on which it runs. While hardware is still important, devices have developed to the point where the differences between compromised and secure networks, databases, and files come down to code, not physical security measures. 

One thing that has not changed since the earliest days of computing, however, is the rapid rate at which technology develops. Likewise, the importance of keeping up is a major factor for any business that hopes to stay relevant or secure. Due to this, as well as the high cost of proprietary software tools, open-source software (OSS) has come to dominate the world of coding. 

What Is Open-Source Software?

In the world of software development, the term “open-source” refers to any software with accessible source code that anyone can modify and share freely. Protocols, algorithms, and even fully-developed programs and games can be created with open-source coding. 

In most cases, open-source code is adapted and integrated into programs where it can be useful. Because source code is the part of the software that users don’t see or interact with, common open-source code is, at times, worked on by hundreds or even thousands of independent parties that can be used seamlessly without any outwardly-recognizable signs. 

In the early days of computing, there were very few dedicated professional programmers, and so the early internet was almost entirely made up of open-source code. The efforts of enthusiasts and professionals alike were aided by the network effect as the internet grew in popularity, allowing more people to contribute and refine the very protocols that were connecting them. 

Today,  many companies employ in-house software engineers; however, much of the code that we still use relies on the efforts of open-source developers. In fact, a 2019 report by Gartner found that 96% of codebases contain at least some open-source code. 

Advantages of Open-Source Software

There are many reasons why open-source coding is still so common. When compared to private development, open-source programs have many advantages. By giving programmers direct access to a program’s source code, the software can be continuously improved and expanded. This allows developers to add new features and fix bugs as they arise, rather than having to rely on the software’s original developer to address these concerns. 

The ability to grow and adapt quickly is essential to success in today’s increasingly fast-paced work environment. Organizations attempting to stay on top (or simply keep up with the market) have needs that evolve rapidly. Because of this, many companies look for solutions with the least amount of friction between development and implementation. 

Dangers of Open-Source Software

For all of the advantages that open-source software brings, there are a number of very significant risks stemming from the very aspects that make it so adaptable. And as prevalent as open-source coding is, a staggering number of organizations lack the structure to address these risks. A 2022 report by the Linux Foundation found that less than half of businesses had an open-source security policy in place for OSS development or usage. 

This lack of preparation can open the door to a wide variety of cyberattacks. Because anyone can access the source code of these programs, any flaws or vulnerabilities could quickly become public knowledge. Malicious actors can also freely examine the code that underlies any programs utilizing a piece of open-source software. 

The exploitation of these vulnerabilities can have wide-ranging negative impacts on all sorts of businesses. Everything from proprietary business data to private medical records can be compromised by attacks utilizing loopholes in open-source code. 

On a more sophisticated level, there are numerous ways in which open-source code can be compromised by hackers, causing anyone who then uses it to fall into their hands. For instance, if a code is compromised before it is used, any flaws built into it will remain there unless specifically eliminated. This may sound simple, but the reality is far more challenging. Unless security experts know precisely what to look for and where to look for it, detecting malicious lines of code can be virtually impossible. Even attempting to do so requires knowledge of whether the code has been compromised to begin with. In most cases, however, vulnerabilities do not become known until they have already been exploited. 

Types of Open-Source Security Risks

To better understand how the aforementioned attacks can occur, let’s examine some of the most common methods that hackers use to inject malicious code into open-source programs. 

Upstream Server Attacks 

In upstream server attacks, malicious entities infect a system “upstream” as it is uploaded onto a computer system or device. To accomplish this, malicious code is added to the software at its source, often through a malicious update, infecting all users “downstream” as they download it. 

Midstream Attacks 

Midstream attacks are fundamentally similar to upstream attacks, but instead of tampering with code at its initial source, they target intermediary elements. These include software development tools and updates that pass on the malicious code from there. 

CI/CD Infrastructure Attacks 

Another variation of the upstream attack model, CI/CD infrastructure attacks introduce malware into the development automation infrastructure of an open-source code requiring “continuous integration” or “continuous delivery” steps. 

Dependency Confusion Attacks 

Unlike the previous three types of attacks, Dependency Confusion Attacks exploit private, internally-created software dependencies by registering a new dependency with the same name in a public repository with a higher version number. The malicious code is then optimally placed to be pulled into software builds in place of the latest legitimate version of the software. 

Case Study: Log4Shell

Regardless of whether hackers compromise open-source code by one of the above methods or learn of a genuine loophole from an open hacking forum, once a door has been opened, any and all data within the compromised system is immediately vulnerable. Some measures can be taken to avoid some of these, but even the biggest companies have fallen prey. 

One of the most dangerous and well-publicized instances of open-source software falling vulnerable to attack came in 2021 when a code-execution vulnerability exploit for Log4j was released. At the time, Log4j was a virtually ubiquitous open-source utility used in countless popular applications, including Microsoft, Amazon, and Twitter servers. 

Referred to as “Log4Shell,” the vulnerability was first reported in November of that year after being identified in the popular game Minecraft. The code exploit was also published in a tweet a few weeks later, leading to numerous forums warning users that hackers could execute malicious code on servers or clients running the Java version of Minecraft. 

Millions of servers were left vulnerable by the exploit. The Apache Software Foundation assigned Log4Shell the highest-possible severity rating in the Common Vulnerability Scoring System (CVSS), and the director of the US Cybersecurity and Infrastructure Security Agency (CISA) called the exploit a “critical” threat. Using Log4Shell, attackers were able to install blockchain crypto, steal system credentials, and access sensitive data before a patch was released. 

Truly Secure Data with Sertainty 

The simultaneously derivative and interconnected nature of the modern internet makes avoiding open-source code a practical impossibility. For this and other reasons, traditional perimeter security falls notably short when it comes to keeping malicious actors out of your system. 

Because of this omnipresent threat, Sertainty leverages proprietary processes through its UXP Technology that enable data to govern, track, and defend itself – whether in flight, in a developer’s sandbox, or in storage. These UXP Technology 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 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 future-proof approach to 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

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.

Is Blockchain Really as Secure as it Seems?

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.

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