Is our organization ready for AI?

AI algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content. LLM is a type of generative AI that has been specifically architected to help generate text-based content. It is not new nor first applicable as back in 1996 MIT published one of the earliest examples of an AI language model (Eliza)

LLM emerged in 2017 and uses transformer models, which are neural networks commonly referred to as transformers. With a large number of parameters, the transformer model and its ability to understand and generate accurate responses rapidly make AI technology broadly applicable across many different domains, which has had a significant impact on our lives.

We can Identify many eras of efficiency, approvement, and effectiveness in any field by just using neural networks. This provides us the ability to detect repetition, identify any anomaly, and manage it accordingly, as there are many use cases where ML is in place today and can still be put in place tomorrow, even in a world where generative AI exists.

In my previous experience, we tested multiple solutions for customer and business operations, although the decision was made based on immediate progress. In this journey, we find out that many fantastic companies have great solutions, some in the early stage and some in the advanced stage.

In a recent article from Gartner, I learned that IBM Watson, like many other competitors, AmplifAI, Lucidya Wipro Holmes, Microsoft Copilot 365, AWS Lake Formation, and, of course, Responsible AI, ZSmart Digital BSS, and MindTitan, are efficient tools with limited integration efforts. Still, CSP is not limited to business requirements.

Any Fintech is challenged with Data structure Lakehouse for data governance with an intelligence engine that combines generative AI with the unification benefits of a Lakehouse to power a Data Intelligence Engine that understands the semantics of our data.

Why is it so hard to achieve? Why has this become a blocker to any organization? I believe this is due to a lack of understanding that data drives the organization.

1.?????? CFO requirement to reduce costs:? Great, let's start collecting data by our data engineers, creating unified data, and ask our data scientist to start working on our utilization as this is NOT a DevOps skill to create a corporate data governance

2.?????? CTO requirement to add AI tools: Amazing, assuming security and Privacy approved the operational tools to be used, let's start with a simple repetition of code writing as we don’t need to invent the wheel each and every time, utilizing this will reduce cost by 40% in the first year and up to 60% in the second. Embracing more accurate code and efficient executing

3.?????? ?CSO would like to run the environment securely: Fantastic, as our tasks are a repetition of an automated task, why not embrace new security-based AI tools to conduct emerging threats detection and SOC\NOC\Application Perf management fusion center proactive approach

Cybersecurity risk associated with Gen AI

Gen AI is a growing concern as the technology becomes more prevalent. Here are a few key risks:

• Security Operation Risks: If we use Gen AI tools for defense, why will the attackers not use them as well? Exposing each and every slit, crack, vulnerability, and lack of patching?
• Development Process: Rapid deployment of generative AI applications can bypass normal software development controls, leading to potential security vulnerabilities.
• Data Breaches: Generative AI tools may unintentionally expose personal or corporate information, increasing the risk of data breaches and identity theft. Did you ever read Microsoft or AWS T&C for using AI Tools? Understanding the telemetry, we are providing by design? Training the algorithm?
• AI Application Security: New applications can introduce vulnerabilities within networks, which cyber attackers could exploit.
• Advanced Malware: AI can be used to create sophisticated malware that adapts to different targets and environments, making it harder to detect and neutralize. Just imagine how easy it is to collect your public metadata to motivate you to
• Evasion Techniques: Generative AI can develop evasion techniques that bypass security systems, such as polymorphic and metamorphic malware.
• Phishing and Social Engineering: AI-enhanced phishing attacks can be more convincing, tricking users into divulging sensitive information.

Business risk associated with Gen AI

Most importantly, Embrace a new paradigm and unite departments to stay competitive. We should adopt a more collaborative way of thinking as this involves breaking down silos and fostering collaboration among different areas of the business.

It is important to create a robust governance framework to address guidelines and protocols for decision-making, accountability, and compliance.

Data team, please understand data sets, algorithms, and limitations. The functionality of algorithms and the limitations of the technology may demand transparency in reporting, so organizations should be prepared to provide insights into their AI processes.

Effective AI initiatives require collaboration between parties and consultation with various stakeholders, including legal teams, C-suite executives, boards of directors, compliance officers, and HR professionals.

As part of the BRCC, we need to develop an internal governance framework that considers risks across all use cases of Generative AI. This framework should enable quick adjustments to ensure compliance and address any issues that may arise during implementation.

Last but not least, organizations need to comply with data privacy regulations when using Generative AI. This requires establishing mechanisms to protect user data and ensuring that their AI systems adhere to data protection laws.