Tag: Artificial Intelligence

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

Posted on by Nicole Tidwell

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 Legacy systems and password-based authentication methods have become especially easy targets for attackers, as outdated software and weak passwords can provide cybercriminals with an open door to an organization’s sensitive data.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.

Emerging Data Security Threats to Watch in 2024

Posted on by Nicole Tidwell

In today’s digital world, data is the lifeblood of organizations. It fuels decision-making, drives innovation, and is at the heart of every successful operation. However, as we march forward into 2024, the landscape of data security is more challenging and dynamic than ever before. New data security threats, both technological and human-driven, are on the horizon, demanding heightened vigilance and innovative solutions. 

The ability to proactively recognize and mitigate these threats is key to both protecting your most vital assets and maintaining regulatory compliance. In this article, we’ll examine some of the most prominent emerging data security threats to watch in 2024 as well as how organizations can comprehensively address them. 

Top Emerging Data Security Threats in 2024

AI-Powered Cyberattacks: The Rise of Adversarial AI

Artificial intelligence (AI) is a double-edged sword. While it powers many of our conveniences, it can also be harnessed for malicious purposes. In practical terms, AI technology has given rise to two distinct data security threats.

Firstly, generative AI’s ability to create convincing, human-like personas has made social engineering threats increasingly difficult to detect. The newfound accessibility of sophisticated machine learning tools also makes it easier for hackers to set an AI program to break through firewalls by trying endless combinations of possible credentials in “brute force” style attacks. 

This is not to say that AI is all bad news for cybersecurity. New tools, such as the innovative Protective Cloud Services (PCS) platform from GuardDog AI, can scan and analyze network traffic, proactively automating incident response steps to save precious time when responding to perimeter breaches. 

The Ransomware Evolution: Double Extortion and Beyond

Ransomware is evolving, and it’s not just about encrypting your data anymore. Ransomware capabilities and tactics have undergone a significant transformation in recent times, extending beyond the conventional act of encrypting data and posing even more potent threats to organizations. 

One notable evolution in this malicious strategy is the adoption of “double extortion” tactics. Instead of merely locking data away, cybercriminals are now leveraging the stolen data as an additional weapon in their arsenal. This entails a two-pronged approach. Alongside encrypting the victim’s data, attackers also threaten to publicly expose sensitive information unless a ransom is paid. This strategic shift underscores a fundamental realization made by cybercriminals — that data is not just valuable to the targeted organizations, but can be equally valuable to the attackers themselves. 

Supply Chain Vulnerabilities: Data Risks Beyond Your Control

The global digital supply chain is intricate, and data flows through it like a circulatory system. But it’s also a point of vulnerability. Attacks on this supply chain can have catastrophic repercussions, extending far beyond the organization directly targeted. 

The interconnected nature of supply chains means that a breach in one part of the network can potentially impact the data and operations of countless partners, creating a domino effect of data risks. As supply chains become increasingly global and digitally driven, safeguarding sensitive data throughout this intricate web has become crucial.

IoT and IIoT Devices: A Growing Attack Surface

The Internet of Things (IoT) is expanding exponentially, but so are its security risks. This is true in both private applications and the Industrial Internet of Things (IIoT). These devices collect and transmit data, which, while extremely useful, also widens the network edge, increasing the number of potential entry points into your system. 

Improperly secured IoT and IIoT devices can quickly transform from convenient tools to potential entry points for cybercriminals seeking to exploit vulnerabilities. These devices frequently lack robust security measures, leaving them susceptible to a variety of threats. Whether it’s a smart thermostat in a home or a sensor controlling a vital manufacturing process in an industrial setting, the security of these devices is paramount. 

Quantum Computing: A New Frontier for Cyber Threats

Quantum computing, once a realm of science fiction, is now becoming a reality. As we inch closer to practical quantum applications, the implications for data security are profound. Current encryption methods, which rely on the computational difficulty of factoring large numbers, may crumble in the face of quantum algorithms. 

Data security has traditionally provided a layered defense against intrusions. This is largely predicated on the assumption that a sufficiently layered defense-in-depth framework can counter intrusions. However, these defenses are built on computational limitations that quantum computers are poised to obliterate. Once useable quantum computing capability reaches the hands of malicious actors, the standard security algorithms that guard much of our most sensitive data today could be effortlessly decrypted. 

Insider Threats: The Danger Within

Insider threats, whether due to malice or negligence, are a persistent concern. These dangers even emanate from people you trust — your employees, contractors, or business partners. The issue with insider threats is that they’re not easy to spot because they’re coming from within your trusted circle.

Whether it’s someone intentionally leaking sensitive data to competitors or a well-meaning employee accidentally clicking on a malicious link, the result can be disastrous. When addressing insider threats, it’s not about securing your network’s external perimeter; it’s about safeguarding your internal secrets from those you trust the most.

The New Foundation of Data Resilience

As we move into 2024, the evolving data security landscape is both promising and perilous. New technologies bring unprecedented opportunities, but they also open doors to novel threats. In this era of data-driven decision-making, one thing is clear: improving data security to match these emerging threat vectors is not a luxury, but a necessity.

In the face of these powerful new data security threats, incremental improvements to existing network perimeters are insufficient. Instead, leaders are looking toward a new paradigm of data security. 

To address these and other mounting data security threats, leaders have begun to approach data as not just something to be safeguarded by perimeters, but as a vigilant protector in its own right. This means that data takes on an active role in looking after itself. So, whether your data is sitting safely within your company’s computer systems, floating up in the cloud, or traveling to another business, it’s always watching out for threats. 

By integrating data-level security into your cyber defense strategy, you create a resilient fortress around your most valuable asset — your data. In the face of quantum computing, AI-powered attacks, evolving ransomware, complex supply chains, IoT vulnerabilities, insider threats, and regulatory mazes, data-level security remains your constant and reliable guardian. Instead of relying on outer defenses, you have an inner champion that keeps your data safe no matter where it is.

In the words of Sertainty CSO Amir Sternhell, “The Sertainty UXP Technology is setting the standards in the IIoT world by protecting and maintaining the integrity of a sensor command to overcome the acceleration in phishing, fakes, and sabotage, attributed to adversarial AI. Rest assured that this upcoming year will witness a glut of holistic Data-Chain-of-Provenance and Digital Twin implementations — premised on the Sertainty Zero-Trust design principles — to quell intrusions into our Industrial Control Systems (ICS) and ransomware attacks.” 

Staying Ahead of Data Security Threats with Sertainty

As a leader in data-level security and self-protecting data technology, 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 data solutions that are 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 adapt and grow to defend sensitive data. Security threats may be inevitable, but with Sertainty, privacy loss doesn’t have to be. 

Understanding and Responding to Different Types of Social Engineering Threats

Posted on by Nicole Tidwell

Social engineering is a deceptive and manipulative tactic used by cybercriminals to exploit human psychology and gain unauthorized access to sensitive information. In the current digital age, where personal and financial data is at risk, it is crucial to be aware of various social engineering threats and take steps to protect ourselves. 

Today, we’ll take an in-depth look at social engineering, explore some common types of social engineering attacks, and discuss solutions to protect your data from imposters. 

What Is Social Engineering?

At its core, social engineering is an art of deception. Instead of exploiting vulnerabilities in computer systems, social engineers manipulate individuals into divulging confidential information or performing actions that benefit the attacker. By preying on human psychology and trust, they gain access to personal, financial, or sensitive data. 

These types of threats can be particularly difficult to mitigate using traditional security systems because there is no “hole in the code” that can be patched to solve the issue. That’s not to say that there is no solution to social engineering attacks; rather, addressing them in a truly secure way requires a more holistic approach than simply increasing perimeter security. 

While they can vary greatly in tactics, most types of social engineering attacks have common goals. These typically involve gaining access to your systems in order to steal or tamper with valuable information, commit financial fraud, or compromise the security of individuals or organizations. 

Types of Social Engineering Threats

Before we discuss how to keep your data safe, it’s important to understand some of the types of social engineering attacks your data may face. While there is no end to the potential number of ways in which scammers can attempt to gain your confidence, the following are a few of the most common examples you may encounter. 

Phishing

Phishing is one of the most prevalent types of social engineering attacks. These attacks involve impersonating a trustworthy entity, such as a bank or a popular online service, to trick individuals into revealing sensitive information like usernames, passwords, or credit card details. Phishing attacks are typically carried out through emails, text messages, or fake websites designed to resemble legitimate ones.

Common phishing techniques include sending deceptive emails that mimic reputable organizations, creating fake login pages to steal login credentials, and using urgent or alarming language to prompt immediate action without stopping to assess the source of the message more thoroughly. 

Pretexting

Fundamentally similar to phishing, pretexting involves creating a false scenario to deceive individuals into sharing confidential information. The attacker creates a pretext to gain the target’s trust, often assuming a false identity to sell the narrative. They may pose as a co-worker, customer support representative, or contractor to manipulate victims into revealing sensitive data or performing actions that compromise security. 

Pretexting attacks often involve the scammer doing research and playing a slightly longer game to help them establish credibility. This allows them to leverage personal or emotional connections, and create a genuine sense of urgency. 

Baiting

Baiting attacks lure people into taking specific actions with an enticing or appealing offer. Unlike the previous two types of social engineering attacks, which primarily prey on fear, baiting exploits people’s natural curiosity or greed to trick individuals into compromising their security. 

Different forms of baiting attacks include leaving infected USB drives labeled as important files, offering free downloads of pirated software that contains malware, or enticing users with the promise of prizes or rewards in exchange for sensitive information. While this may seem somewhat far-fetched, research has shown that hardware-based baiting scams can be a particularly effective social engineering threat vector. 

Tailgating

Tailgating is an even more physical type of social engineering attack. Also known as piggybacking, these attacks involve an attacker gaining access to secure servers by physically following an authorized person through secured access points. This type of attack exploits the natural human tendency to hold the door open for others or be polite, allowing the attacker to gain unauthorized entry.

Tailgating attacks can occur in various settings, such as office buildings, data centers, or restricted areas. By blending in or exploiting moments of distraction, the attacker bypasses security measures and gains access to sensitive locations or systems.

Impersonation

A more in-depth alternative to tailgating is impersonation, where attackers assume the identity of legitimately authorized parties to gain access to your databases. Attackers may pose as employees, customers, and service providers in order to be let into places where they can then hack into your databases more easily.

Methods employed by impersonators can include using fake email addresses or phone numbers, manipulating caller IDs, or creating realistic social media profiles to establish credibility. 

Quid Pro Quo

Perhaps the most direct form of social engineering attacks, quid pro quo exploits involve an exchange of something valuable in return for sensitive information or access. Rather than tricking targets into unwittingly granting them access, attackers promise a benefit or favor in exchange for personal or confidential data. This type of social engineering attack often targets employees within organizations. 

Examples of quid pro quo schemes can include a scammer posing as an IT support technician offering free technical assistance in exchange for login credentials or an attacker promising a substantial discount or exclusive access to a service in return for sensitive financial information. 

Protecting Against Different Types of Social Engineering Threats

As we mentioned above, addressing social engineering threats requires a fundamentally different approach than other areas of cyber security. Increasing the strength of passwords or introducing measures to prevent software-based attacks such as cache poisoning is completely ineffective when hackers gain access to your databases using legitimate credentials. 

Contrary to popular belief, protecting your data from social engineering attacks also requires more than training. While employee training is a common step used to counter all types of social engineering scams, the human component often remains the weakest link in your security system. To this point, a 2022 study of different types of social engineering attacks concluded that “providing awareness against SE-based cyberattacks is not sufficient.” 

Zero Trust Data Security

The true key to solving all types of social engineering attacks is rethinking our entire approach to cybersecurity. Traditionally, the focus of digital privacy systems has been to keep outsiders from accessing the private networks and stores where data is hosted. While there will always be a place for maintaining this security perimeter, relying on this alone leaves all data within vulnerable to anyone who has already gained access to the servers or data files. 

This is where a Zero Trust framework for self-protecting data can be of the most use. Rather than simply trying to improve on perimeter measures, self-protecting data reimagines the entire approach to security. 

As the name implies, the goal of self-protecting data is not just to keep hackers out of your system but to create truly secure files. Instead of being left accessible to any “trusted” users, self-protecting files themselves are coded with the ability to recognize malicious activity and counter it immediately, regardless of who appears to be performing the action. 

Empower Your Data with Sertainty

Sertainty leverages proprietary and patent processes through its Data Privacy Platform and core technology that enable data to govern, track, and defend itself — whether in flight, in a developer’s sandbox, or in storage. These technology protocols mean that even if systems are compromised by AI tools 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. With the proliferation of human and AI threats, security breaches may be inevitable, but with Sertainty, privacy loss doesn’t have to be.

Data Chain Custody Part 2: AI Data Security History, Flaws, and Emerging Solutions

Posted on by Nicole Tidwell

Recently, we discussed emerging open-source AI threat vectors, including the proliferation of potential open-source threats to private servers and data chains. Today, we’ll take a closer look at the history of AI data governance and discuss whether emerging trends in the marketplace can address them. 

When it comes to data security, AI presents a whole new field of dangers. But despite the high-tech nature of the data protection industry, even leading companies and government agencies are burying their heads in the sand and relying on existing security protocols to manage these threats. Regardless of whether or not your organization is on board with AI, these tools are here to stay. Reports have predicted that the AI market will experience a shocking Combined Annual Growth Rate (CAGR) of between 20.1% and 32.9%. As such, data privacy methodologies must pivot to take these AI tools into account.

AI Data Gathering and Security 2013–2023

While the underlying principles of artificial intelligence have existed for a long time, the widespread emergence of usable AI tech is less than a decade old. Depending on your definition, you may consider early algorithms introduced in the 1990s to be a precursor to current machine learning tools, but many experts generally regard 2013 as the origin of usable “deep learning,” as we now know it. 

The primary revolution at this stage was the use of five convolutional layers and three fully-connected linear layers and parallel graphics processing units (GPUs), as well as the introduction of a more efficient rectified linear unit for activation functions. 

The following year, in June 2014, the field of deep learning witnessed another serious advance with the introduction of generative adversarial networks (GANs), a type of neural network capable of generating new data samples similar to a training set. Essentially, two networks are trained simultaneously: (1) a generator network generates fake, or synthetic, samples, and (2) a discriminator network evaluates their authenticity.

2017 saw the introduction of transformer architecture that leverages the concept of self-attention to process sequential input data. This allowed for more efficient processing of long-range dependencies, which had previously been a challenge for traditional RNN architectures. 

Unlike traditional models, which would process words in a fixed order, transformers actually examine all the words at once. They assign something called attention scores to each word based on its relevance to other words in the sentence.

Generative Pretrained Transformer, or GPT-1, was introduced by OpenAI in June 2018. Since then, the program has gone through numerous evolutions. While OpenAI has not disclosed the specifics, it is assumed that the current iteration, GPT-4, has trillions of parameters. 

Emerging Trends in AI Data Security

On the other side of the same coin, some data security companies have already introduced tools utilizing the same AI protocols. These programs utilize the information-gathering and analytical capabilities of machine learning to identify potential threats and suggest courses of action to mitigate them. 

However, it’s important to note that — despite the use of new, powerful machine learning technology — the fundamental premise of this solution is based on a conventional understanding of data security. The system’s proactivity only extends as far as any traditional perimeter security and threat analysis (albeit in a more efficient manner). 

This inherent inadequacy means that even the most sophisticated form of conventionally-minded AI security can (theoretically) be exploited or circumvented by the same means as their predecessors.  

As such, truly addressing all potential threat vectors requires a complete rethink of how secure data governance is handled, rather than applying new technology to existing systems. 

AI-Informed Secure Data Governance 

Though many “leading” commercial tools rely on outdated security structures, a better solution is already available. Unlike traditional data privacy, Zero Trust security provides a proactive method for mitigating attacks. 

The key differentiator between Zero Trust and other, more traditional solutions is letting go of the (incorrect) assumption that sensitive databases can be secured simply by keeping malicious actors out. 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. 

With specific regard to emerging AI threats, the core Sertainty UXP Technology empowers data chain custodians to opt in or out of the use of Personal Identifying Information (PII) by AIs like ChatGPT. This ensures that organizations exposed to ChatGPT — as well as their employees and clients — maintain privacy, regulatory compliance, and protection in all scenarios. 

Sertainty UXP Technology also allows developers working with open-source AI programs like those from OpenAI to maintain their own privacy commitments by giving data files the ability to protect themselves and generating repositories of those who approve the processing or those who wish to opt out of data sharing.

Even regulators have taken notice of the shortcomings inherent in today’s cybersecurity paradigm and expressed interest in this new way of approaching data privacy. Prompted by both real and potential dangers, including AI threat vectors, an Executive Order titled “Improving The Nation’s Cybersecurity” has outlined the need for US federal agencies to move toward a zero-trust security model. 

Sertainty Data Privacy 

In the current landscape of trendy tech and buzzwords, concrete solutions are more vital than ever. Sertainty Zero Trust technology enables secure data governance and the training of AI models with a tried-and-true multi-layer security solution.

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 by AI tools 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.

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.