Every disruption is a double-edged sword. On the one hand, it brings many positive changes from which to build. On the other hand, it also comes with exploits that expose organizational vulnerabilities and sabotage policies. When not addressed, the latter can lead to the bad outweighing the good—which is why regulations always follow every disruption.
Having transitioned from rule-centric systems to neural networks, artificial intelligence is surging worldwide. With global corporate investment in AI reaching $92 billion in 2022, there is little doubt that the technology will be an integral part of enterprise tech stacks and new business models. Therefore, AI is on its way to normalization intended to ensure its fair, safe, and transparent use. To prepare decision-makers for the change and point out the main vectors of change, this article will outline the AI Trust, Risk, and Security Management (TRiSM) framework and its significance for enterprises investing in AI.
What is AI TRiSM and why is it necessary?
First mentioned by Gartner, AI TRiSM is a set of rules and policies focused on embedding AI governance models firmly into business environments, relationships, and interactions.
The goal of AI TRiSM is to address numerous safety and transparency concerns about artificial intelligence and outline a comprehensive approach to using AI for business purposes.
As the abbreviation implies, the framework is responsible for upholding trust-based AI models and maintaining necessary risk and security management.
The concept of AI TRiSM isn't innovative in itself. Every new technology offers more than opportunities. It also disrupts the established norms and policies because of its features, which can be exploited for malicious intent. The most basic example would be the internet's and online services' evolution. The increased number of data theft and privacy breaches followed the next-level convenience and collaborative capacity. As a result, organizations must adhere to GDPR and the CAN-SPAM act, while providing safety guidelines for employees to follow whenever they go online.
Albeit disruption regulation is a common practice, the need for a framework that lays the groundwork for safe and fair use of AI results from consistently growing techlash.
Techlash is a term for distrust and negativity toward technology, which became considerably stronger in the following years.
- In 2021 the trust in the technology dropped from 78% to 57%
- 66% of EU users don’t know what happens to their personal data
- 46% of the US citizens believe that AI will have a negative impact on their lives
Such a decline in trust shows that people are past the hype for innovation—now, they want normalization and clarity. There is a demand for responsibility and transparency, which, when unaddressed, can become a significant challenge for the technology sector.
However, there is a silver lining for businesses. According to a 2024 report by Edelman, 60% of respondents trust businesses to introduce innovation in a secure, beneficial, and understandable way. Meanwhile, nongovernmental organizations, media, and governments were rated much lower. Following these results, business leaders have a precious opportunity to nurture that trust and shape a new culture that synergizes technology and talent, combining the best of both worlds.
Frameworks like AI TRiSM are the first step in that direction, as they proactively respond to people's concerns and inject security into leveraging artificial intelligence.
The pillars and principles of AI TRiSM
Aside from “What is AI TRiSM?”People also ask whether AI TRiSM is a technology for regulating artificial intelligence or a code of conduct.
In truth, it's both.
Any new technology that involves working with massive amounts of data and private information requires some installed precautions and educating employees on risk management. The AI TRiSM framework is similar in that it combines technical measures and behavior guidelines to improve the experience of operating with artificial intelligence via four fundamental pillars.
1) Explainability/model monitoring
Explainability is a problem when it comes to artificial intelligence. AI models are very complex and hard to define.
Essentially, an AI system is a black box—inputs come in, and outputs come out. The most complicated part is that developers can't guarantee that AI will use the data exactly as intended, so there are cases of AI platforms giving biased or inaccurate responses.
Due to this issue, researchers often suggest slowing down the pace of AI development.
The more complicated an AI model is, the harder it is to track down how it interprets data and makes conclusions. Generally, the model performs as expected as long as the output is correct and meets the goals. However, constant monitoring is necessary to identify budding bias and inaccuracies in time. While it's not possible to dissect an AI model bit by bit and give a 360° explanation of how it works, it doesn't mean that the technology shouldn't be explained.
What can business leaders and executives do to make AI more explainable to employees and end users?
- Articulating the purpose
With AI TRiSM technology is made understandable by enabling people to explain it in their own words without resorting to terms and cookie-cutter phrases. The priority of an AI TRiSM framework is to educate employers on the function of the model, its target audience, and its main objectives. Knowing the goals and pain points an AI system is supposed to cover helps employees establish the technology's place within the enterprise. - Explaining weaknesses
Believing that an AI system is perfect and delivers accurate results can lead to a flawed mindset that endangers workplace efficiency. Every AI model has its weak points in addition to strength, and end users must stay aware of the former. To ensure timely identification of bias and inaccuracies, executives need to ensure that employees always check and verify the results delivered by the model. In addition to providing much-needed quality control, such practice also instills confidence among workers by showing them that AI is a tool that still relies on human vision and assistance to perform. - Showing the data
Employees need to know what data and practices were used to train the AI model, so it's important for executives to provide that information. This step allows a better understanding of any bias that may emerge and allows for tracking expected and needed outcomes.
2) Model operations
Explaining AI models is only a part of ensuring their optimal performance. Another crucial element of the AI TRiSM framework is creating processes that manage AI systems across every stage of their lifecycle.
Human vigilance goes hand in hand with technology. This is how you get the best results from every innovation-related effort. AI models are the same in that regard. Given that more enterprises are leaning towards private AI models, they need to establish an infrastructure and an environment that ensures full synergy of their AI system with enterprise operations.
Deployment
Processes that ensure the successful launch of the model for the intended enterprise department, its interaction with the target application, and the ability to secure the needs of stakeholders and cover key priorities.
Reproducibility
Exploring the model's performance and measuring its success will help define how it can be replicated for other goals and enterprise processes.
Diagnostics
Testing the model to confirm its smooth performance and that algorithms were selected correctly.
Regulatory compliance
Establishing the governance and compliance framework for the model to keep it consistent with enterprise policies as well as state regulations.
Scalability
Adapting the model to grow and change with the enterprise through continuous data enrichment, integration, and testing.
Business application
Selecting datasets that meet the enterprise's business goals and injecting them into the model.
Management and monitoring
Observing the model’s performance after deployment, measuring results and introducing adjustments.
3) AI application security
Each change comes with new potential security exploits and vulnerabilities. When it comes to AI models that process important business data, there is an increased risk of data leaks and security breaches due to several factors:
- Use of third-party tools
Working with AI models by third-party vendors can potentially endanger enterprise data. Since such AI models absorb inputs and use them for training, that can lead to various data leak scenarios. For instance, an enterprise AI model can reveal confidential information from another organization within an output—because this information becomes part of the datasets available to all models by the provider. To prevent such scenarios, enterprises should either work with providers who implement mechanisms and precautions to protect sensitive data absorption or use custom AI models. - Malware attacks
AI makes an impactful difference in cybersecurity and risk management. Unfortunately, it gives an edge to phishers and cybercriminals who exploit the technology’s vulnerabilities and turn it into a Trojan horse for enterprises. According to the report by Manchester Metropolitan University, AI models can be compromised at every stage of their lifecycle.
Stage
Weakness
Risk
Design
- Open-source code vulnerabilities
- Source code vulnerabilities
- Weak authorization protocols
- Improper input validation
- Incorrect output encoding
- Biased training data
- Malicious code injection
- Data poisoning
- Unauthorized system changes
- Data theft and corruption
- Phishing, compromised use
- Inaccurate outputs
Development
- Limited programming models
- Source code vulnerabilities
- Unsecured data storage
- Lack of proper restrictions
- Input perturbation
- Poor AI asset protection
- Embedding hidden exploits
- AI model manipulation
- Data interception
- Unauthorized access to model
- Compromised performance
- Access to AI model
Deployment
- Low endpoint security
- Poor physical/digital component setup
- Non-encrypted data transmission
- Improper cloud services setup
- Incomplete model evaluation
- Malicious input injection
- Disruption of model functionality
- Data theft
- Copying private AI models
- Model tampering
Maintenance
- Delayed security patching
- Concept drift
- Malicious personnel activity
- Non-systematic logging
- Compromised data safety
- Reduced model effectiveness
- Data theft, model sabotage
- Undetected model issues
- AI-generated malware
In addition to compromising AI models, cybercriminals also weaponize AI to create a new type of malware. This tactic considerably expands their approaches to phishing, impersonation, and data theft, making a drastic impact on the cyber threat landscape. Depending on the goal, AI-powered malware can copy confidential digital products, impersonate users, and manipulate files—all while encrypting its activities and being very hard to detect. This type of threat is expected to surge and evolve, revealing more advanced varieties of AI malware by 2025.
Nothing debunks the perfect AI myth as the amount of errors and attacks it's vulnerable to. Hackers and phishers know that artificial intelligence is a tool that can be used or sabotaged to their advantage. Therefore, executives must also have this mindset when adopting AI. Just like any tool that processes sensitive data, it requires all the safety guidelines, authorization steps, and measures available, so it could deliver value without side effects
4) Privacy
Privacy remains one of the most controversial subjects regarding AI models. While artificial intelligence is lauded for extracting and processing unstructured data, people are concerned about how it knows what data it can extract.
- Unethical personal data gathering
Sometimes, employees submit personal data in private surveys to improve the workplace environment. There are also wellness checkups that are confidential and prioritize employees' well-being. However, this information can become a part of a dataset meant for training AI models. As a result, private employee or user information can be revealed to third parties in just one query. Since AI users are usually not explained how their system stores and processes information, it adds to their anxiety and reluctance to interact with technology. - Intellectual property violation
The loud backlash against image-generating AI platforms is the most illustrative example of why such boundaries are needed. According to a recent leak, Midjourney used the works of over 16,000 artists without permission to train its AI model. Such actions create a dangerous precedent that threatens the intellectual property and working rights of content creators. Studios laying off illustrators and replacing them with AI platforms already exist—and they are particularly abundant in countries that don’t have strict AI regulations. - Covert surveillance
AI face recognition technology is another large privacy concern. In addition to the natural aversion to being watched and monitored, employees are also worried about bias and hallucinations that aren't uncommon in AI systems. Additionally, if some AI image recognition programs are trained with simplified images, it can lead to inaccurate results and even reputational damage. Therefore, it creates distrust for enterprises that use AI-powered surveillance in the workplace.
Indeed, there is a mindset that AI can replace employees and help businesses bypass some policies. However, it's flawed and harmful. First, companies that adopt such a practice are likely to experience an eye-opening backlash after employee turnover increases and a drop in productivity. Second, we already established that AI can be twisted and poisoned at the design stage. Therefore, relying on it in all aspects without scrutiny and monitoring can jeopardize the entire enterprise. So, seeing AI as the ultimate artificial worker rather than an instrument for teams to use can harm organizations immensely. It's important to remember that AI can optimize time and cut costs by assisting employees in their tasks, preventing errors, and removing unnecessary steps from key operations. AI TRiSM examples show that technology should work for people—not the other way around.
The benefits of implementing AI TRiSM methodology
Given the principles of AI TRiSM mentioned above, it may seem that the entire framework exists to introduce limitations to AI and enforce regulations. However, there are also multiple advantages gained by embracing AI TRiSM technology.
- Increasing revenue
Within TRiSM AI isn't constrained—it's empowered to perform smoothly and safely, delivering accurate outputs and enriching analytical reports. Accordingly, the technology can cover the ultimate goal—elevating enterprise operations and converting productive performance into profit. These conclusions aren't unfounded: Gartner predicts that companies implementing the AI TRiSM framework will improve their business outcomes by 50%. - Optimizing costs
Artificial intelligence accelerates organizational processes and identifies the most rewarding tasks and goals to focus on. As a result, organizations can avoid wasting time and resources on activities that aren't worth it. However, what is more important is that a properly secured AI saves costs by preventing financial loss. By embedding safety protocols and AI model monitoring, executives protect their companies from immense reputational damage, often followed by million-dollar compensations. - Nurturing trust
Around 68% of customers believe that companies investing in advanced AI models should be able to prove they are trustworthy. Also, around 63% of customers expressed concern about the AI bias. Logically, if an AI adopter fails to meet the demand for honesty and transparency, their clients will choose another vendor. So, preserving trust through fair AI policies, ethical use of the technology, and respect for privacy is crucial to securing lucrative and long-term customer relationships.
When a novel technology enters the scene, some adopters enter the Wild West of innovation: they aim to master the disruption and use it before any rules appear. Moreover, they strive to become the ones making these rules. Nevertheless, the ones making the rules are people: clients, employees, and target audiences. If the innovation doesn't have regulations and isn't safe to use, it will lose momentum. So, the right way to maximize the value of an innovation would be to handle it responsibly and with consideration for end users.
How to implement AI TRiSM?
Establishing an AI TRiSM framework begins before implementing an AI model and doesn't end after the model is deployed and monitored. Preparing the enterprise for the transformation in advance is imperative--and a matter of organization, knowledge, and collaboration.
- Assembling a dedicated team
Proper task distribution is the first step to building a working AI TRiSM framework. It makes sense to organize a unit that will be committed to developing, embedding, and testing policies as well as researching AI TRiSM examples. Additionally, this dedicated team should communicate change to other departments, educate employees on necessary guidelines and norms, and inform them about potential risks. - Keeping documentation on AI models
Preserving valuable information on AI model development and processes in documents will facilitate employee education and make the technology more explainable. It is also incredibly useful for tracking performance and identifying bias. - Prioritizing data protection
To ensure a transparent and honest approach to AI, organizations need to safeguard their systems against data theft, manipulation, and disruption. This requires rigorous testing at every stage of model development, data encryption, multi-factor identification, and permission-based access. Avoiding generic safety routines and personalizing guidelines for each specific model and data type is also necessary. - Putting risk management at the core of AI TRiSM technology
Solutions for data integrity shouldn't be an afterthought. They need to be central in designing, developing, and deploying an AI model, identifying errors and data anomalies while securing accuracy. - Combining diverse perspectives
Adopting AI isn't just about technology. It's also about business vision, legal requirements, ethics, and data purity. Due to this, building a functional AI model is successful when numerous experts from various areas participate in the process. Consulting with a lawyer can ensure the model complies with the latest regulations and meets necessary transparency standards. Working with business analysts allows us to configure the model to cover all business goals and employee needs. Collaborating with professionals familiar with integrating AI technology into enterprise environments enables synergy with core enterprise operations.
An AI model reflects your teams' and professionals' experience and knowledge. So, it's highly important to create and implement a methodology that synergizes machine capabilities and human agility while contributing to your enterprise's ethics and policies. The more perspectives and minds participate in developing such a methodology, the better fit it will be for your organization.
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