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

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.

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. 

Securing Private and Intelligence Data

When it comes to information security, no sector can be overlooked. Both private sector and intelligence data gathered by government agencies require care in their handling, storage, and transmission. And while there are a number of universally-accepted best practices for maintaining data confidentiality, the unique nature of information relevant to national interest necessitates additional measures. 

Much of the work of information security is the result of policy and training, but tools like the Sertainty Data Privacy Platform also play a central role in securing data in both the public and private sectors. 

What Is Intelligence Data?

Generally, intelligence data refers to any data gathered by intelligence operatives or agencies. This data can be collected for a variety of purposes, from predicting and mitigating potential threats to informing government policy and even military operations. This can include information about people, finances, transportation, infrastructure, or any other data that may be of use in a particular scenario. 

Often, the identities of the agents gathering the data, as well as the methods used, are highly protected. This amplifies the need for airtight privacy, as each step of the process must remain strictly confidential, even from other agents within the organization. 

Similarities Between Private and Intelligence Data Security

At its core, data privacy is a universal concern. Any organization, whether public or private, that gathers information relies on a certain level of exclusivity in order to make that data useful. Not only is secure data vital to making informed decisions, but it can also provide a business edge over the competition. Likewise, in many industries, information security protocols are required in order to obtain (and maintain) the licenses and certifications needed to conduct business. 

When it comes to creating an organizational security policy in the modern world, there are a number of factors that need to be accounted for — whether you’re protecting private or intelligence data.

Defense-in-Depth Safeguards

The foundation of any organization’s security plan, regardless of its industry, can’t be one-dimensional. A defense-in-depth approach combines multiple levels of security protocols into a single, cohesive privacy plan. This can include elements such as firewalls, encrypted networks, security training, and any other layer of protection. 

Two-Factor Encryption

Another vital piece of the puzzle in a comprehensive security plan involves user authentication. Users may be familiar with the process of imputing a code received on a separate device, but two-factor authentication can include even more secure measures, such as physical access keys, biometric scans, and answering security questions. 

Remote Access Protocols

Unlike in the past, virtually all data storage networks need to be accessible to users outside of a specific office or closed LAN. This can apply to work-from-home employees and intelligence field operatives alike, and ensuring that only approved users can enter the system is vital. Furthermore, both of the above concepts around safeguards and encryption can and should play a role in how remote access protocols are designed. 

Special Considerations for Intelligence Data

The above represent some of the most common security measures, all of which can be found in many civilian applications. Others, however, are less common outside of high-sensitivity industries. 

There are two primary factors that make intelligence data different from other private information. For one, the potential implications of an intelligence data leak are far greater than those in any private company. Consequences can be felt on a national or even global level. This level of significance means that there is absolutely no room for mistakes of any kind. 

The second factor is the need for multi-level confidentiality. As we mentioned above, in addition to the data itself, the identities, locations, and methods by which it was obtained are often extremely sensitive. Due to the need for internal privacy, conventional perimeter security is often insufficient. 

Let’s take a look at some of the unique ways in which intelligence data can be protected, as well as examine the value of Zero-Trust security. 


Perhaps the most critical element of intelligence data security strategies involves keeping different sources and stores of information separate from each other. The reasons that compartmentalization is so important are twofold. Firstly, even if one data store is compromised, compartmentalization ensures that the breach is contained to that single, limited store. The other primary benefit is that users have less potential to interact with each other, allowing for an increased level of anonymity. 

Asymmetric Access

Rather than relying on a secured messenger application, sending sensitive communications in the intelligence world is often handled using asymmetric access. In these types of systems, two virtual keys are needed to receive messages: one public key, findable within a database, and one private key, accessible to only a specific designated user. Sending messages can be done using a public key, but each user’s private key is needed to open the messages intended for them.

Sensitive Compartmentalized Information Facilities

In the most sensitive cases, extremely important data can only be accessed within the confines of a Sensitive Compartmentalized Information Facility (SCIF). To gain access to the information stored in these physical locations, users must be pre-screened and authorized, as well as pass through a series of checks and authentications. Once inside, they can access and discuss the information stored there but cannot send or receive any communications while they are in the facility. 

Zero-Trust with Sertainty

In virtually every area we’ve discussed, traditional network security falls short in a number of key areas. Insider threats, human error, and a number of other inevitable vulnerabilities can leave information of all kinds open to malicious actors. Unlike other technology platforms, which are fundamentally limited in their scope, Sertainty data protection is ideal for both intelligence data and private applications. 

Self-protecting data from 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. 

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