5 GenAI Observability Mistakes That Will Limit Your GenAI Applications
The tide of GenAI adoption is sweeping across the globe, as more enterprises and their employees are starting to see the technology as a viable way for them to improve productivity and revenue. Here at Portal26, we empower enterprises to do this in a way that serves to ensure they see the full potential of their investment, by essentially providing them with an in-depth view of how GenAI applications are being used across the organization, by department, all the way down to the individual.
The kind of multifaceted observability that we are able to provide is a fundamental aspect of successful adoption, as without it, enterprises will compromise their own security, while also experiencing a diluted version of the true potential of GenAI.
We’re about to build the case for improving GenAI visibility by explaining why it’s such an important aspect of your investment, using 5 common mistakes that many enterprises can fall foul to.
1. Blocking GenAI systems without making any further security provisions
When GenAI is used responsibly and effectively, it serves to enhance operational processes and efficiency, but there’s already a subcurrent of organizations that are weaponizing the technology to achieve the opposite kind of outcome. The default response from enterprises against malicious GenAI usage is to simply block external GenAI systems, but this measure is extremely weak in the face of how advanced these systems are and blunts the organization’s ability to compete with companies that are leading the way in adoption.
For example, malicious GenAI systems can be engineered to bypass even the most robust Secure Web Gateways and authentication protocols, so your sensitive data is compromised – even when you think you’ve invested enough to protect it. The multi-layered authorization system that your enterprise has in place can be redundant in a few seconds, all by GenAI technologies that are able to lie to gain access to restricted places. There’s a ‘trojan horse’ likeness at play here; as the technology is smart enough to mirror your own internal personnel with uncanny accuracy.
2. Having gray areas when it comes to understanding exactly how GenAI is being used
Early observability is essential for establishing strong governance and mitigating risks associated with GenAI. Many organizations underestimate the importance of monitoring GenAI usage from the outset, leaving themselves vulnerable to potential issues.
Gray areas around how and when GenAI is being used is exactly where these issues arise, so it’s therefore crucial to understand how data is flowing in and out of your organization, especially during the development and testing phases of GenAI. Without visibility into data sources and usage patterns, you risk exposing sensitive information, violating intellectual property rights, or infringing on copyright.
Once GenAI models are built and deployed, the challenge expands. AI-generated content or the information it is fed, can be easily shared, copied, or modified, making it difficult to track its lifecycle.
Comprehensive observability is therefore necessary to monitor content movement, identify potential security breaches, and ensure compliance with regulations. By establishing robust observability practices from the start, organizations can proactively identify and address potential risks, protect sensitive data, and optimize GenAI performance throughout the enterprise.
3. Using GenAI tools in silos
Those already deploying GenAI are likely familiar with the specialist tools available, from GitHub, Microsoft to Adobe and Chat GPT, but beyond these user-friendly interfaces, there’s a huge analytics gap. Organizations can’t readily access usage statistics, and they aren’t able to access data on the prompts being used, or the attachments that are being uploaded to these systems. Knowing what people are ‘feeding’ GenAI in their prompts is one of the hardest aspects of observability, as every organization will have its own variables to factor in.
The gap in analytics, for both the system and for those using it, means that GenAI applications can happen in a ‘silo’ environment. This is one of the most ineffective ways to use the technology, as it provides you with no information on the bigger picture:
- Where are the opportunities to add to applications?
- Where are applications performing the best?
- What kind of input generates the best type of output?
- What inputs are being used and for what purpose?
- What type of training is needed to improve outputs and minimize risk?
4. Creating your own GenAI powered tools without accompanying user management systems
There’s an increase in the ‘hands-on’ approach to GenAI, with many enterprises taking tool development into their own hands, creating LLM’s from existing APIs, or occasionally from scratch, to suit their own purposes. While there’s nothing at fault with this concept, problems arise when user management isn’t factored into the process. In this scenario, individuals are faced with a whole melting pot of considerations including:
1. Authentication
What login mechanisms are being used? Have passwords, biometrics, or multi-factor authentication options been explored?
2. Authorization
Are role-based access controls being used, assigning permissions based on user roles (e.g., admin, editor, viewer). Are these settings supported by fine-grained permissions, i.e deciding what actions users can perform, such as generating content, viewing reports, or modifying settings.
3. Activity monitoring
Are regular GenAI audits carried out, and are logs kept? Are all anomalies tracked? What extent of user analytics are available?
4. Compliance and ethics
Does the system adhere to data privacy laws? Have measures been implemented to protect user data? Is there transparency for users about how their data is used and the AI’s decision-making processes?
5. Security measures
Is the data encrypted both in transit and at rest? Are there incident response plans in place to respond to security incidents or breaches?
Proceeding without this kind of caution for your own self-developed GenAI model poses a few questions – Is GenAI still a superpower if we can access and create systems independently? Or are we all better off by having every resource necessary to understand the capabilities of this impressive technology?
5. Not investing in sufficient GenAI education for those deploying the tools
Investing in GenAI is an exciting point for any enterprise, as it can serve such a dynamic range of purposes. But with each usage comes a different kind of training requirement, and moving ahead without this knowledge will be detrimental to the success of your overall applications. Education needs to be a key pillar of your scaleable GenAI budget, and it should be addressed in a uniform way; everyone responsible for deployment and using GenAI, regardless of seniority, should have the same base level of understanding of the technology and its complexities. The rationale is simple – everyone has access to the same GenAI tools, so the only differentiator is what the inputs are. Garbage in, garbage out.
Investing in GenAI education assures that usage and interactions will follow best practices in terms of your business objectives, as well as GenAI governance, visibility, and GenAI data security.
Invest in GenAI the right way with unrivaled GenAI observability from Portal26
We are here to equip your enterprise with GenAI observability and GenAI visibility capabilities, helping you to benefit from GenAI while we take care of compliance, ethics, and security simultaneously. Our GenAI TRiSM platform fills the gaps not covered by your existing security platform, bringing with it a layer of supercharged, ultra-smart protection and dynamic insights. From safeguarding your system against outside threats, to providing you with real time analytics on your own GenAI applications.
For a first-hand understanding of how we can help you to maximize your GenAI investment, arrange a free demo with a member of our team today.