The world might alter due to generative AI. New businesses, industries, products, and even economies can benefit from it. But what sets it apart from “traditional” AI and makes it superior could also be its downfall.
Its extraordinary capacity for creation has given rise to a completely new range of security and privacy issues.
Do I have the right to the training data? is a new question that businesses are suddenly forced to address themselves. To the model? To the results? Do the system’s internal permissions extend to newly produced data? How are that system’s rights safeguarded? How can I control data privacy in a generative AI model? The list continues.
It is not surprising that many businesses are being cautious. Many people have argued for an outright ban on these technologies due to their glaring security and privacy flaws as well as a reluctance to rely on temporary fixes. But there is still hope.
The solution to the more intricate and critical security issues that large language models (LLMs) face is confidential computing, a novel method to data security that safeguards data while it is in use and preserves code integrity. It will enable businesses to fully utilize the power of generative AI without sacrificing security. Let’s first examine what makes generative AI particularly vulnerable before we continue.
A knowledge-rich subset of a company’s data can also be ingested by generative AI to create a queryable intelligent model that can instantly generate new ideas. Although this has a great deal of appeal, it also makes it very challenging for businesses to maintain control over their confidential information and adhere to changing legal requirements.
It’s no longer enough to encrypt database fields or form rows; protecting training data and models must come first.
Inadequate data security and trust control could unintentionally weaponize generative AI for misuse, theft, and illegal usage due to this concentration of information and the resulting generative outcomes.
In fact, employees are increasingly putting regulated information including source code, customer data, and private corporate papers inside LLMs. In the event of a breach, the fact that these models are partially trained on new inputs could result in significant intellectual property leaks. Additionally, any content that a business is contractually or legally required to keep private could leak if the models themselves are compromised. In the worst situation, stealing a model and its data would provide a rival or nation-state actor the ability to replicate everything and steal that data.
The stakes are really high. In a recent study, Gartner discovered that 41% of organisations had encountered a security or privacy incident involving artificial intelligence, and that over half of these incidents were brought on by an internal party compromising data. Growth in these numbers is inevitable with the introduction of generative AI.
In addition, when businesses invest in generative AI, they must stay current with changing privacy laws. The need to maintain compliance with data obligations is strong across industries, as is the incentive to do so. When it comes to enhancing patient outcomes and overall effectiveness in the healthcare industry, AI-powered personalized medicine, for instance, offers enormous promise. However, accessing and using vast volumes of sensitive patient data while remaining compliant will be necessary for clinicians and researchers, creating a new conundrum.
Generative AI requires a new security basis to solve these issues as well as the others that will unavoidably arise. It’s no longer enough to encrypt database fields or form rows; protecting training data and models must come first.
Evidence of the integrity of the code and data—and the trust it conveys—will be vitally crucial in circumstances where generative AI conclusions are employed for critical decisions, both for compliance and potentially legal liability management. The entire computation must be completely protected, as well as the environment in which it runs.
The advent of “confidential” generative AI
Confidential computing provides a straightforward but incredibly effective solution to what would otherwise appear to be an unsolvable issue. Confidential computing entirely isolates data and IP from infrastructure owners and restricts access to only trusted apps and trusted CPUs. Encryption protects data privacy even while being executed.
In fact, data security and privacy have become inherent features of cloud computing, to the point that even if a hostile attacker compromises infrastructure data, IP and code are absolutely undetectable to that bad actor. The security, privacy, and attack threats of generative AI are perfectly mitigated by this.
A security game-changer, confidential computing has been gaining ground recently. Leaders at Azure, AWS, and GCP have all proclaimed its effectiveness, and every major cloud provider and chip manufacturer has invested in it. Now, the same technology that is persuading even the most adamant cloud sceptics may be the answer to generative AI’s secure takeoff. Leaders must start to take it seriously and recognize its significant effects.
Businesses may be confident that generative AI models they utilize will only learn from the data that is contained in confidential computing. Full control and assurance are provided by training with private datasets from a network of reliable sources across clouds. Behind a company’s own four walls, all information—whether it is an input or an output—remains totally secure.
