AI can be very powerful yet without the right controls in place, it can detract staff away from their jobs rather than enhance their performance. The key is to ensure information from the AI is securely provided to the correct associate that has the necessary permissions to review the answers from the AI.
This article will address some of these security concerns and how AI can be implemented to add value in a secure and compliant manner.
As a special bonus, a white paper on the detailed steps of how to apply AI security can be accessed through this newsletter!
What are Sources of the risks in using AI
In contact with inner management circles, there is a growing anxiety with AI as more leaders are getting worried that this technology, which is estimated by the Bank of America to $15.7 trillion by 2030 (BofA Reference Article), is passing them by. Yet, many still remain on the sidelines, as they grapple with the potential risks associated with letting AI disseminate information to their organization with little or no control.
In fact, Mckinsey recently conducted a flash survey of more than 100 organizations and found that “63 percent of respondents characterize the implementation of gen AI as a “high” or “very high” priority. Yet 91 percent of these respondents don’t feel “very prepared” to do so in a responsible manner” (McKinsey Reference Article). This is indicative of many companies that we regularly converse with. The executives are wary of unleashing AI without controls and for good reason.
These are just some of the risks involved with implementing AI:
AI will divulge sensitive corporate information.
It’s a real concern. If not properly controlled with limited access to applications, data lakes and databases, unvetted data can eventually make it to the AI and be distributed without anyone knowing it. This poses a great risk, especially around trade secrets.
Unauthorized personnel will get access to data.
Even if the data is controlled, it is still possible for associates to be given access to information that was not intended simply because the AI had access to it. This can happen without the associate or those in charge realizing that this information is being disseminated.
Associates can be misled by uncontrolled AI responses.
If AI is let loose without any controls, the chance that “false narratives” can enter company discourse is high. A recent paper published in AI and Ethics stated that there may be “significant cyber security threats generative AI models pose, including potential ‘backdoors’ in AI models that could compromise user data or the risk of ‘poisoned’ AI models producing false results” (AI and Ethics Reference Article). That’s why it’s so important to ensure there are controls AND security in place when implementing AI. The ways that can be accomplished will be discussed later in this article.
AI will require new IT security infrastructure-structure and drain many resources.
There is a common perception that these complex AI systems require critical security infra-structure to be built, specific expertise hired and modifications to existing systems required in order for AI to be benefit. This belief cements the thinking that AI drains resources, time and money that could be used to just get the job done in the traditional manner.
AI learnings and data can leak outside the private corporate network.
Above all, many associates and even their leaders are “quietly” concerned that even the perception of AI implemented in their department could result in layoffs as executives work to trim costs to increase company profitability. This stark outcome hangs on the heads of many leaders I speak with as they try to balance their team’s workload while not being left behind by the advancing AI technology.
There are many more security concerns that need to be addressed with AI. The list above is by no means inclusive of all of them. What’s even more profound than the risks are the way there are perceived in the business community. The next section will address that.
Observations on the Security of AI
In consultation with many large and mid-sized companies, it is clear that security is on their minds when they think of AI. There is always the worry that AI will go wild and uncontrolled interaction with their staff will result in chaos in the workplace. Some professionals I spoke to stated, and I paraphrase:
“What if the AI responds in an inappropriate way?”
“How can we control the narrative of the AI to ensure it defines company terms the way we want it to?”
“What if the use of our private and restricted AI encourage associates to use the unrestricted public once’s to get more functionality?”
“How can we limit the AI about mis-informing our associates on regulations that then puts the company at a compliance risk?”
“AI records question-answer conversations that pose a security risk depending on who has access to them.”
The trouble is that the concerns above are valid and without a secure, controlled AI, a company could be facing one or more of the risks mentioned. The next section and white paper attached goes into some steps that can be used to make AI implementation safer for an enterprise.
Leveraging High Performing AI in a Secure Manner
The common misconception that we often hear, that a company controlled, restricted AI is less performing than the open public ones is not always the case. In fact, if designed and implemented properly, the AI can unleash incredible productivity as outlined in our previous newsletter (Higgins Newsletter Article 1).
Fortunately there are lots of non-technical AI security material like the one produced by the NCSC that provides guidance on how to safely proceed with AI (NCSC Reference Article).
We will highlight what we believe to be some of the key points to address to balance AI security with performance. Some are obvious while others are less intuitive than our clients expect:
Review AI Security Risks Reduction Strategies
Implement Machine Learning Security Measures
Secure Model Training Practices
Audit and Monitor Machine Learning Models
Design and Implement Cybersecurity Strategy for AI Systems
Ensure Privacy Protection in AI
Utilize Privacy-Preserving Technologies for AI Applications
Install AI-Powered Malware Detection
Conduct Ethical Hacking in AI Systems
Use Proven Ethical Hacking Techniques for AI Security
Ensure Responsible Disclosure of AI Security
Design and Review AI Security Checklist for Enterprises
A detailed Higgins AI Directive White Paper can be downloaded and will address many of these topics in the list.
Artificial Intelligence security is critical for success and must accompany any sized enterprise AI strategy.
The next section and this weekly Newsletter will provide practical, implementable use case examples of how high performing AI can be applied using these security points to validate its use in everyday work life.
Validating AI Controls in Business to Maximize Security
Let’s take a look at how AI can be securely implemented into company operations. Review this slideshow to see real-life examples !
These are only quick snapshots of the main steps to ensure success in any enterprise-wide Artificial Intelligence and Machine Learning initiatives.
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