Secure GenAI begins with trusted data. Incorporating a logical data management approach early in your GenAI projects can help mitigate security threats and ensure robust data governance.
We have seen it before – new technology has become widely adopted, and along with it comes a slew of havoc-wreaking opportunities for cybercriminals. Email scams surging with the rise of Microsoft 365 and crypto-mining exploits increasing with the growing popularity of Infrastructure-as-a-Service (IaaS) are just a couple of recent, well-known examples. The same is happening with Generative AI (GenAI).
Generative AI (GenAI) is a hot topic that’s not only discussed in mainstream media but also throughout industries and is heard around the globe. In fact, McKinsey & Company defines it as the year that organizations truly began using and getting immense value out of GenAI. McKinsey & Company projects that AI will automate half of all work between 2040 and 2060 and expects that GenAI will accelerate this timeline by a decade. In a study conducted by IBM’s Institute for Business Value, nearly all executives who responded believe that adopting GenAI will increase the likelihood of a security breach within the next three years.
What Are the GenAI Emerging Threats?
While GenAI has demonstrated a multitude of technological benefits to organizations, it’s also fostering a new wave of cyberthreats, enabling bad actors with more opportunities to exploit vulnerabilities and carry out attacks.
Before GenAI became mainstream in 2020, researchers discovered GenAI-powered malware like DeepLocker, which used advanced obfuscation techniques to evade detection. ChatGPT has also been linked to new threats; a study from the University of Illinois Urbana-Champaign found that a GPT-4-based agent successfully exploited 87% of “one-day” vulnerabilities and publicly disclosed vulnerabilities for which patches are not yet available.
GenAI now has the ability to make previously inconceivable situations a reality. Bad actors can impersonate voices, faces, and personalities, making their attacks more convincing. Imagine receiving a video message from your manager requesting specific actions or a call from a bank asking for urgent payments.
See also: With AI, It’s a Complex Future for Cybersecurity
Defending Against GenAI-Driven Threats
Organizations should adopt a robust framework for securing AI systems, starting with updates to governance, risk, and compliance strategies. As AI regulations become more stringent, embedding these types of principles upon commencement of every project can not only accelerate innovation but also enable a strong security foundation.
Ensuring data security is fundamental to the reliability of GenAI. Because data is the cornerstone for GenAI models, it’s a prime target for cyberattacks. Bad actors can tamper with data to misdirect or manipulate business decisions, presenting organization leaders with new legal, security, and privacy challenges.
See also: Superior Data Analysis Needed for the Cybersecurity Industry
The Case for Logical Data Fabric
Organizations often start GenAI projects with a single data source, such as a vector database. However, to harness the full potential of GenAI, it’s essential for the GenAI application to have access to data across multiple distributed systems, and in a variety of formats. For example, for a customer service chatbot to provide accurate responses, it would need to pull information from a variety of systems and platforms: enterprise resource planning, support ticketing, customer relationship management, and internal application programming interfaces.
Established security measures like authentication, encryption, and masking will continue to be critical. Additionally, robust access controls are necessary to protect sensitive data. It’s crucial to be able to implement role-based access control or attribute-based access control and to leverage data tags indicating sensitivity.
A logical data management approach surmounts these challenges. This approach helps to consolidate disparate data through metadata, offering a unified view while maintaining security and governance. It supports user and role-based authentication and authorization, with row-based and column-based security options, including data masking. It also enables stakeholders to track the lineage of data and queries, aiding regulatory compliance.
Remember, secure GenAI begins with trusted data. Incorporating a logical data management approach early in your GenAI projects can help mitigate security threats and ensure robust data governance.
