Generative AI is the perfect embodiment of the double edged sword. Yes, the consensus is clear: tools like Bard and ChatGPT are sparking a surge in productivity in the enterprise, spanning cybersecurity, customer experience, sales and more. It’s a boon on one side of the spectrum.
Now, let’s turn our attention to the other side—the risks. From accidental data leakage to ethical concerns and AI-enhanced cyber-attacks, generative AI creates just as many concerns as it does opportunities.
In fact, a Gartner survey found that AI tools clinched the spot as the second most-reported risk among C-suite leaders in Q2. That means AI tools make a significant debut in the top 10 and are likely to stay a key risk on the corporate horizon for the foreseeable future.
So, how can companies stay one step ahead? How can they swing the balance in favor of success? How do they harness the power of AI to mitigate, rather than increase, risks?
Generative AI: The risks
Generative AI innovations like natural language processing (NLP) are improving the efficiency, accuracy, and speed of cybersecurity defense. Think: better threat hunting, phishing detection, user behavior analysis, and much more.
However, generative AI is just as useful for the bad guys as it is the good guys. In the same way cybersecurity vendors are leveraging AI to enhance their products and solutions, cyber criminals are experimenting with the technology to launch new types of attacks and evade traditional security measures.
But, it’s not just threat actors that create generative AI risks, the technology itself is vulnerable to biases, data leakage, and compliance issues.
Here’s a deeper look at some of the major risks associated with generative AI:
- Enhanced social engineering: Generative AI can easily be misused by malicious entities to fabricate convincing fake content, including deep fakes. This, in turn, lays the foundations for attackers to create eerily realistic social engineering attacks that are near impossible to detect until it’s too late.
- Malware generation: Already, there have been instances of individuals manipulating SaaS-based generative AI models to produce ready-to-go malware. In one case, security researchers managed to use ChatGPT to create highly dangerous polymorphic malware, designed to outsmart defense mechanisms.
- Bias: AI models grow and develop from the data they process. If this data is outdated, incomplete, or tainted with bias, it can skew results and compromise security. Biases may lead to false positives or hinder the identification of genuine security risks.
- Data leakage: Enhancing generative AI relies on ingesting user input data as training data. This inadvertently creates a gaping cybersecurity vulnerability should confidential information be processed or rewritten. For example, imagine an insurer using a generative AI platform to draft a confidential email with patient data or a graduate inputting financial information into a tool for a presentation. The AI, if queried by a third party, could generate responses including the confidential data it has been trained on, which would be a data breach and, potentially, a compliance fine.
Enter zero trust
To remedy the risks of generative AI and unlock the benefits, organizations need to fight fire with fire. That is, carefully use generative AI to combat the very risks it poses.
The way to do this? Applying generative AI through the lens of zero trust.
Until now, many organizations’ zero trust journeys have been hindered by yesterday’s technologies. But, generative AI has shifted the stage, empowering organizations to “never trust, always verify” users at the data access level in real-time and autonomously.
Of course, zero trust is not a simple case of click and install. It’s a set of principles, requiring a fusion of technologies and tools that must work together cohesively. In that sense, moving to a zero-trust architecture takes time and investment.
To prevent boost success in adopting this type of architecture, organizations should start with low-hanging fruit before graduating to more complex use cases.
With that in mind, here are the applications where generative AI and zero trust can have an almost immediate positive impact on risk mitigation:
- Data loss prevention: Natural language processing (NLP) can enhance the efficiency and accuracy of data loss prevention (DLP) solutions, enabling organizations to automate the process of discovering, classifying, and protecting unstructured data in apps collaborative SaaS apps and AI tools. Polymer DLP for AI harnesses the power of generative AI to seamlessly and intelligently redact unstructured, in-motion sensitive data within generative AI platforms like ChatGPT and Bard.
- Behavioral analytics and anomaly detection: Generative AI can quickly gather data about users’ “normal” or baseline behavior. This enhances the proficiency of security tools spotting potential security threats and data exfiltration in real time.
- Real-time user education: Generative AI can revolutionize security awareness programs, bringing point-of-violation training to users who accidentally–or intentionally–thwart a data protection policy. For instance, Polymer DLP provides real-time nudges to users when they perform risky behavior. This approach has proven to reduce repeat violations by over 40% within just days.
With great power comes great responsibility
Ultimately, generative AI is an incredibly powerful tool and it must be deployed carefully to ensure data integrity, compliance, and cybersecurity. By focusing on using generative AI to build a zero-trust architecture, organizations can lay the foundation for secure innovation and unlock the benefits of this technology, while mitigating the potential risks.
