May 22, 2025
How ioSENTRIX vCISO Differs from Traditional vCISO Services?
How ioSENTRIX vCISO Differs from Traditional vCISO Services?
Fiza Nadeem
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Artificial Intelligence (AI) has become a real and important part of our lives. It is no longer just an idea from science fiction movies. In 2023, there was a big progress in AI technology, especially with Generative AI. The world's largest companies are now competing fiercely to create their own AI systems and models. The goal is to improve productivity and achieve better results than ever before.
The excitement around AI is justified. A recent report from McKinsey estimates that the Generative AI industry could create between $2.6 trillion and $4.4 trillion in value in the coming years. With AI having possible uses in almost every major industry, it shows a promising picture of a future with more automation and higher productivity.
However, this future depends on how well AI is managed and controlled.
Like any new technology, one important challenge with AI is understanding and measuring the risks it brings. For businesses that want to use AI's benefits, knowing about these risks is not just about following rules. It is also very important for their overall strategy. The way they handle these risks can affect their finances, daily operations, and reputation for a long time.
Unfortunately, only a few organizations have learned this lesson firsthand. They have faced important operational problems caused by their lack of experience with the technology and the absence of a clear set of guidelines for responsible use.
Examples like Morgan Stanley restricting staff from using ChatGPT due to concerns about false information, Samsung banning employees from using GenAI tools after sensitive company information was uploaded, and the Dutch "toeslagenaffaire" scandal (thousands of Dutch citizens were wrongly penalized for child care benefits fraud by a self-learning algorithm) all show the same issues.
According to a recent report by Gartner, organizations that build a secure and trustworthy AI system are twice as likely to succeed in adopting AI and achieving their business goals.
Therefore, avoiding AI is not a good option to consider.
An AI risk assessment is a thorough and flexible process that adapts to changes in the AI environment and the specific needs of a business. It helps identify all the potential risks an organization might face and supports the creation of strategies to effectively reduce those risks.
Introducing AI into an organization’s current processes can bring notable risks and difficulties. The most critical and immediate concerns associated with this include:
Model Poisoning
Bad actors use a technique called model poisoning to interfere with an AI model’s learning process. They do this by adding false or misleading data to the training dataset. This causes the model to learn wrong patterns, which can lead to incorrect results.
Bias
Bias in AI models happens when the results are unfair because of biased assumptions in the training data. This bias can show up in many ways, like racial, gender, economic, or political bias. Usually, the bias comes from the training dataset, which may not be neutral or may include existing prejudices.
When AI produces biased results, it can cause problems in important areas where AI is used to make decisions, such as hiring, loan approvals, and criminal justice.
Hallucination
Hallucination in an AI model happens when the output is false or incorrect because it was trained on poor-quality data. The result may seem clear and logical, but it is actually made up and not real. This happens because the AI has limitations in understanding context and depends on patterns it learned during training.
Prompt Injection
A prompt injection attack tries to manipulate an AI model’s outputs by changing the input prompt. This is often done by hiding or disguising the input data in a way that causes the model to give a wrong or biased response. As a result, the outputs can be false, misleading, or unfair.
Prompts DoS
Hackers use a Denial of Service (DoS) attack to cause AI models and systems to respond automatically. This type of attack can overload the system, with the goal of making it crash when a harmful prompt is executed.
Exfiltration Risks
Hackers and malicious actors might use specific words and phrases to figure out and expose training data used by AI systems. They can gain access to a compromised dataset and then analyze it to find and exploit any sensitive information contained within.
Sensitive Data Risks
AI models need a lot of training data to work properly. Often, this data includes personal and sensitive information. If this data is not properly protected, it can be at risk of breaches, unauthorized access, or misuse. This can lead to privacy violations, damage to companies, or identity theft.
Therefore, it is very important for organizations to encrypt the data and set up strong access controls to keep the training data safe.
Data Leakage
Data leakage happens when test data (which should not be part of the training process) accidentally influences the AI model. This can cause problems like overfitting and poor performance on new data. It can also risk exposing private information such as messages, financial details, or personal data if the data is not carefully managed.
Non-regulatory Compliance
Many organizations are still trying to find the best way to use AI effectively while following data protection rules. This challenge is made harder by new AI laws being introduced around the world, which means organizations need to follow different rules depending on where they operate. Failing to meet these rules can lead to legal penalties and damage to the organization’s public trust.
As AI grows in ability and use, it is very important for organizations to regularly carry out thorough risk assessment. These checks help ensure AI is used safely and the organization follows international AI laws as closely as possible.
Although it can be difficult because there are many different AI laws around the world, these regulations are important. They provide organizations with a basic framework to help manage the specific risks that come with AI advancements. Regular risk assessments are also essential for balancing innovation with protecting customers’ digital rights.
While the term "AI regulation" covers many rules and laws, the global situation shows a lot of variety. This is because different countries have their own cultural, ethical, and social values that these laws need to consider and follow.
Important laws like the European Union’s AI Act, the GDPR, and Canada's Artificial Intelligence Data Act (AIDA) each set clear and strict rules. These regulations focus on data privacy, obtaining user consent, and guiding organizations to use AI systems responsibly, especially when these systems impact data and user rights.
In contrast, the United States has taken a more relaxed approach. Without a federal law, individual states and government departments are responsible for creating, applying, and reviewing their own rules and guidelines related to AI.
However, the United States might be moving toward a more consistent approach. This is indicated by Executive Order 14110, called “Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence,” which was signed by President Joe Biden in October 2023.
The order explains how the US federal government plans to be more active in AI development and management. It also asks all government agencies that use AI to help support these goals. A key part of this is requiring each agency to appoint a chief AI officer to oversee AI-related efforts.
Although different AI rules will require organizations to perform various checks and actions to follow the laws, risk assessment is one of the main parts that is included in all of these regulations.
A good AI risk assessment is a detailed process that carefully reviews all AI models, systems, and features used by an organization. The goal is to find and reduce possible risks in areas like security, privacy, fairness, and responsibility.
Bias Assessment: It is important to deal with bias in AI systems and models, especially in the data they use. This involves checking for any unfair or discriminatory patterns in the input data that could cause biased results in the outputs produced by the AI.
Algorithmic Impact Assessment: The focus is on how AI works in practice, especially the results it produces. This includes the decisions it makes, how it uses data, and the suggestions or recommendations it provides.
AI Impact Assessment: It is important to consider the wider effects of using certain AI systems and models. This includes looking at social, ethical, and environmental factors that may be affected by their use.
AI Classification Assessment: The organization needs to identify the types of AI systems and models it is currently using. These should be categorized as low, medium, or high based on how they are used and the possible effect they have on the organization.
Transparency is an important issue in most AI systems, both in how they work and from an ethical point of view. Often, AI systems are called "black box" because the developers do not fully understand how these systems make their decisions.
This lack of clarity makes it hard to properly evaluate how well and how efficiently these systems work. It also causes organizations to depend on guesses when trying to understand the risks that these systems and models might present.
Technology is advancing much faster than the rules and regulations can be created. Every new technological development offers new opportunities, but it also creates new challenges that organizations often find difficult to handle.
Legal rules and methods to assess risks are being created to help organizations deal with the challenges of AI. However, these challenges are always changing.
Following regulations is often very difficult for most organizations. Dealing with various rules at the international, national, regional, and local levels can be overwhelming and strain their resources and operations.
Different regulations may have different legal rules and requirements. It is challenging for organizations to keep running smoothly while still providing high-quality products and services.
AI regulations can be particularly difficult because they are constantly changing, and there is no single global standard for them. Different countries and regions have taken different approaches.
For example, the United States is about to introduce many new federal and state laws related to AI, which shows that organizations of all sizes will face challenges to meet these regulations.
Many AI systems are designed to reduce or replace human involvement in decision-making. While these decisions can be more technically accurate, they often overlook important subjective factors that humans consider. This creates ethical questions that organizations need to address.
Although fairness and bias are important goals in creating better datasets for AI training, organizations still face uncertainty when it comes to the moral issues of AI use. This is especially true in sensitive areas like healthcare and criminal justice, where the ethical impact is significant.
An organization needs to have a clear understanding of its internal AI systems. It should maintain a complete list of all AI models being used, whether they are in public clouds, SaaS applications, or private environments.
After identifying and listing all AI models, it is also important to classify them properly. Organizations can classify their AI models based on their specific needs. Proper classification helps them plan how to manage risks and protect data effectively.
After properly classifying all AI models, the organization can then assess each model for potential risks. This helps not only in meeting global regulations but also in identifying and reducing risks such as:
Once an organization understands all the AI models they are using and the specific risks linked to each, they can connect these models to the right data sources, data processing steps, vendors, potential risks, and compliance requirements. This helps build a strong base for managing AI risks and allows for monitoring of all data movement.
With strong data and AI controls, organizations can carefully manage the inputs and outputs of their models. This helps them spot and address any of the risks mentioned earlier effectively.
Setting up these controls makes sure that any data used in the AI models follows the organization’s data policies. They also help the organization meet other data responsibilities, like:
These controls help manage who can access sensitive data by setting clear rules. They use the Principle of Least Privilege (PoLP) to make sure only authorized people and AI models can reach important data, reducing the risk of unauthorized access.
We recognize that AI risk assessment isn’t just a security checkbox; it’s a strategic necessity. Our approach is designed for forward-thinking organizations that want to embrace AI confidently while staying compliant and secure.
ioSENTRIX delivers comprehensive AI Risk Assessments that go far beyond surface-level scans. We identify model-level threats like bias, hallucination, and prompt injection attacks, while also tackling overlooked risks such as data leakage, non-compliance, and algorithmic misuse.
You don’t just get a report, you get a full strategy that supports your AI governance, security posture, and business continuity. ioSENTRIX can help you build resilient, responsible AI programs.
Contact us for a customized AI risk readiness assessment.
The frequency depends on the specific development and deployment processes of each organization. It is recommended to have regular reviews, with at least one assessment each year, to keep everything up to date. Some organizations may prefer to do assessments twice a year or every three months to better fit their needs.
Yes, AI risk assessment can quickly spot possible sources of bias in the data, processes, or algorithms used by an organization. Once these issues are identified, appropriate steps can be taken to fix them.
An AI risk assessment mainly looks at AI and machine learning systems, including issues like bias, data quality, and ethics. In contrast, a traditional IT risk assessment covers wider IT security and operational risks, such as network security and data breaches.
