Presented by Outshift by Cisco
IT leaders are increasingly prioritizing generative AI, applying it to critical business functions such as marketing, design, product development, data science, operations, and sales. Beyond organizational applications, generative AI also plays a pivotal role in humanitarian efforts, including vaccine development, cancer detection, and environmental, social, and governance initiatives like resource optimization.
However, each of these applications presents distinct security risks, primarily concerning privacy, compliance, and the potential loss of sensitive data and intellectual property. As the adoption of generative AI expands, these risks will only intensify.
“Organizations must proactively plan every generative AI project—both internally and externally—not only for current risks but with future challenges in mind,” advises Vijoy Pandey, SVP at Outshift by Cisco. “It's essential to balance innovation and user trust, prioritizing privacy, authenticity, and accountability."
Understanding the Unique Risks of Generative AI
The risks associated with generative AI are unlike those of traditional technologies. Sophisticated phishing attacks utilizing deep fake technology can mislead individuals, making identity fraud more prevalent. Fraudsters may impersonate a company's customer service agent to illicitly acquire sensitive information, leading to significant financial losses.
Moreover, users often unwittingly input sensitive information into generative AI models, which can retain and leverage this data for training purposes, raising significant concerns about privacy compliance and data protection. Organizations must stay vigilant regarding new regulations emerging in response to generative AI risks.
While the extensive data requirements of large language models (LLMs) pose challenges, existing security frameworks can help mitigate risks associated with raw data and data leaks.
Fraudsters can exploit vulnerabilities within the generative AI pipeline, potentially compromising accurate predictions, denying service, or even breaching operating systems and impacting social media reputations.
“As we confront threats like data poisoning and model inversion, detection becomes paramount,” Pandey explains. “We depend on established confidence intervals to assess model outputs, but if attackers compromise the pipeline, our standards for trust may be undermined.”
Immediate detection of issues is challenging; problems typically manifest over time. Security operations teams have resources like MITRE ATLAS and the OWASP Top 10 to address emerging generative AI security concerns.
“Generative AI is still evolving, and security must evolve alongside it,” Pandey warns. “This is an ongoing journey.”
Intellectual Property Security and Transparency Issues
Generative AI generates complex outputs by processing vast datasets and intricate algorithms. Users typically cannot trace the origins of the information provided, resulting in significant risks related to intellectual property exposure. This concern arises both from external training data and the user-generated content being input into these models.
“The challenge is securing access to data while safeguarding intellectual property and sensitive information from leaving the organization or unintentionally entering the model,” Pandey states. “Are we using open-source or licensed content inappropriately?”
Additionally, generative AI models often lack real-time data relevance and specificity, impacting applications like retrieval-augmented generation (RAG), which incorporates current business context and provides citations for verification. RAG enhances LLMs by enabling them to learn continuously, reducing inaccuracies while keeping sensitive information secure.
“RAG is akin to sending a generalist to the library to learn about quantum physics,” Pandey describes. “The gathering and ingestion of relevant information is essentially what RAG accomplishes. While it comes with challenges and requires experimentation, it effectively customizes foundation models to meet organizational use cases without compromising data integrity.”
Securing Users and Building for the Future
“The landscape of generative AI use cases is currently well-defined, but in the coming years, it will permeate everything we create or consume,” Pandey predicts. “This necessitates adopting a zero-trust approach.”
Assume every component of the pipeline—from data to models, to user access—has the potential for failure.
Moreover, given the novelty of this technology, the lack of established rules means that human error and vulnerabilities can easily be overlooked. Comprehensive documentation is crucial for addressing potential violations and prioritizing security measures.
“Capture your objectives: document data sources, models in production, and their training processes,” Pandey advises. “Categorize applications based on their importance, ensuring security policies reflect this criticality.”
Security must be embedded at every level, from infrastructure to application. If one layer fails, a defense-in-depth strategy can provide additional protection, underscoring that security is a continuous journey.
“The solution to navigating security within generative AI lies in stochastic processes—developing models specifically to manage security concerns in other models,” Pandey suggests.
The Critical Importance of User Trust in Generative AI
User trust is a key business performance metric. “Security impacts user experience, directly influencing the success of generative AI solutions,” Pandey emphasizes. Loss of trust can severely affect revenue.
“An insecure application essentially becomes non-existent; any related monetization or business objectives become irrelevant,” he elaborates. “The same applies to an insecure generative AI model or application. Without security, your model is unusable, thereby hindering business growth.”
Conversely, if customers discover their private data has been mishandled—especially in a generative AI environment—trust can erode rapidly, further complicating the already fragile perception of AI technologies.
These two simultaneous challenges necessitate time for organizations to fully comprehend the risks and establish effective solutions.
“Adopt a zero-trust philosophy, build robust defenses, and remain aware of the opaque nature of generative AI,” Pandey concludes. “Recognize that where you begin today will differ significantly from your position in three years, as both generative AI and its associated security measures rapidly evolve.”
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