AI TRiSM: Challenges and its Framework for Businesses

  • Aswin A AAswin A A
  • Artificial Intelligence
  • 4 months ago

Did you know that every second, artificial intelligence processes more information every second than the entire human race did in the 19th century? 

Today's AI is so powerful that it crunches data and can create almost real responses. Generative AI has made the imagination come true. But this is just the stepping stone for what we should expect.

Earlier, when we used to think about artificial intelligence, we always thought of fully automated systems that worked by robots who could think like us. We don’t know how to make robots think, but we have reached an age where we can automate almost anything.

That’s how AI TRiSM ((Artificial Intelligence Trust, Risk and Security Management) has been formed. But what is it?


What is AI TRiSM?

AI TRiSM stands for - AI Trust, Risk & Security Management.

It is like a structured approach or set of guidelines designed to ensure that AI systems are trustworthy, fair, reliable, and secure. It aims to become a toolkit that can be used to develop and maintain AI systems.

So, when you develop a system for your organization using AI, you have a framework to follow and support them. 

By using a combination of tools and techniques, organizations can better understand and manage the risks of their company that could arise due to these systems made of AI.

It allows organizations to respond to potential threats more effectively and quickly.

AI TRiSM also helps organizations follow ethical guidelines when developing and using AI systems.

As per its name, the TRiSM framework aims:

  1. Ensuring AI models are trustworthy and fair: This means ensuring that AI systems give accurate and unbiased results.
  2. Ensuring reliability: AI systems should work consistently and handle unexpected situations well.
  3. Protecting data: The framework should help keep data safe and respect people's privacy.
  4. Identifying and addressing risks: To help organizations spot and fix problems before they cause harm.


AI TRiSM Framework

The 5 Pillars of AI TRiSM for Software Development

AI TRiSM is the combined development of B2B Software by application of the following five components:


User trust is paramount in B2B software adoption. AI TRiSM prioritizes explainability for all stakeholders, like end-users, managers, consumers, etc., so that the target group can easily comprehend AI-driven decisions. Such transparency fosters trust and helps to collaborate better.


Whatever model you develop, the most critical step is its application and implementation. Thus, efficient deployment and management of AI models are crucial. AI TRiSM, through ModelOps, streamlines these processes, allowing organizations to respond rapidly to market demands. This results in enhanced agility, quicker time to market, and a competitive edge in the dynamic business landscape.

Data Anomaly Detection

An average data breach costs at least $4.45 million, close to Rs. 37 crores.

Not just monetarily security breaches can have severe long-term consequences, not just monetarily, like poor brand image, loss of critical personal data, and potential lawsuits. 

AI TRiSM aims to integrate robust anomaly detection to identify real-time irregularities. By proactively addressing potential threats, B2B software becomes resilient against cyber-attacks. Thus safeguarding sensitive data and maintaining the integrity of business operations.

Adversarial Attack Resistance

An extension of the previous pillar, adversarial attack resistance focuses on cyber attacks. 

As technology advances, so do cybersecurity threats. AI TRiSM focuses on bolstering B2B software against attacks from hackers who want to steal sensitive info, money, passwords, etc.

By using frameworks like TRiSM, companies can ensure that AI models are robust and resistant to manipulation. This proactive approach secures organizations against financial losses and reputational damage associated with cybersecurity incidents.

Data Protection

As seen,  AI TRiSM significantly emphasizes data protection, ensuring that B2B software adheres to privacy standards and regulatory requirements. 

This commitment mitigates legal risks and builds trust with users, enhancing the software's reputation.


Why is AI TRiSM needed?

This model is nothing but imperative today. This is mainly because of the potential risks and challenges that AI poses to the world. Like:

Challenge 1: Lack of understanding that can lead to biases

Most individuals, including managers, users, and consumers, struggle to understand how AI works and its capabilities fully.

AI TRiSM should help customize answers for specific audiences. This framework will break down how a model functions based on its strengths, weaknesses, and likely behavior. 

Its testing should also help us understand the potential biases that could affect the model in the long term. Such an understanding will create transparency that shows how the datasets are used for training and the methods employed in selecting them, which can help uncover sources of bias and enhance comprehension.

Challenge 2: As AI accessibility is increased, the risks increase 

Access to generative AI tools like ChatGPT is already widespread, introducing new and evolving risks. The emerging deepfake videos, which look so real, are just one of them.

AI TRiSM recognizes and addresses significant and dynamic risks for any AI application. Whether hosted, cloud-based, generative AI, or any other. It goes beyond conventional controls, ensuring enterprises can navigate the evolving landscape of generative AI responsibly and effectively.

Organizations can mitigate regulatory, commercial, and reputational consequences by emphasizing transparency, trust, and security in AI operations.

It also integrates risk management processes into ModelOps, ensuring continuous monitoring throughout the AI pipeline. 

Challenge 3: Keeping the AI up to the current knowledge

Making sure AI models can learn continuously is essential but can be challenging.

AI TRiSM advocates for a proactive learning model. It encourages the development of AI systems that actively seek new information and adjust to changing circumstances. By fostering continuous learning, AI TRiSM ensures that models remain adaptable, providing more reliable and efficient performance over time.

Challenge 4: Incorporating the human feedback

AI systems often struggle to effectively use human feedback, making it challenging to improve them.

AI TRiSM steps in by emphasizing a collaborative approach. It promotes the creation of dynamic feedback mechanisms, ensuring that human insights are received and actively incorporated into AI systems. This iterative process allows the models to adapt and evolve, resulting in more accurate and user-friendly outcomes.

To keep things afloat, AI TRiSM strongly emphasizes user-centric design. It proposes the establishment of streamlined feedback loops. Such a system ensures that the user’s insights and feedback are collected and comprehensively analyzed. 


What’s Next in AI Development?

As the AI landscape matures, there are many concerns, along with excitement. That’s when regulatory frameworks take center stage. Laws like the EU AI Act pave the way for comprehensive AI system regulations. 

These frameworks aim to strike a balance between fostering innovation and safeguarding individuals and businesses from potential risks associated with AI.

That’s just how AI TRiSM is trying to shape the world. The framework aligns perfectly with emerging regulations and concerns about applying AI in every automation project.

Everyone wants guaranteed security for their data. That’s what we at Cubet prioritize as number one on our list whenever we take on a new client. So, if you want a partner who develops the perfect B2B automation software, then Cubet might be your ideal partner. Contact us to book a consultation call now!


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