Today, organizations are progressively adopting and integrating artificial intelligence on a large scale – changing how they work daily.

AI can transform your customer service operations in the long run while offering your company fascinating new opportunities. New research indicates that individuals who could use generative artificial intelligence (AI) tools fared 14% better than those who didn’t, and AI in marketing technology is now table stakes.

However, a big question remains: how do you plan to deal with the concerns around information security and trust?

Remember, Businesses are eager to harness the potential of AI while traversing the complex landscape of safe and ethical data utilization. This makes AI cybersecurity a key consideration, even as you augment customer experiences.

The Intersection of AI and Cybersecurity When Managing Customer Data

73% of professionals in sales, service, marketing, and commerce surveyed recently said that generative AI brings in new security risks. Over 60% of those who intend to implement AI must figure out how to do this safely while safeguarding sensitive data. The previously mentioned risks are especially significant in heavily regulated sectors, such as health and financial services.

As customers engage with businesses across various platforms in an increasingly interconnected world, it’s undeniable that organizations accumulate an abundance of customer information.

However, they are legally obligated to protect the data they’ve collected, such as personally identifiable information (PII), personal health information (PHI), and personally identifiable financial information (PIFI), all of which might be used to identify or locate an individual.

A security breach isn’t just a non-compliance incident but also indelibly erodes customer trust and market goodwill. For example, it took the financial services company Equifax nearly a year to recover in the market after it allowed customer data to be compromised.

This challenge is further exacerbated in the context of generative AI. Gen AI produces new data contextually akin to training data; therefore, no sensitive information must be included in this material.

The possibility of generating content that unintentionally violates the PII of an individual remains high if you do not focus on AI cybersecurity and privacy.

Key Cybersecurity Risks in AI-Driven Systems That Marketing Leaders Should Know

Although integrating AI into consumer experiences has several benefits, it poses several cybersecurity risks. These are :

1. Data privacy and protection concerns

Personal information gathered, analyzed, and stored may prove critical to the performance of your AI algorithms. However, illicit disclosures are possible in the absence of cybersecurity measures for AI. An instance of untested code may result in a chatbot displaying an individual user’s purchase history to another user while engaging in a live conversation. This infringes privacy regulations significantly.

An insecure or weak AI system could offer hackers abundant consumer information. Imagine a scenario wherein the encryption of consumer data you store needs to be improved, or access controls haven’t been enforced. This is why 42% of brands consider balancing cybersecurity and customer satisfaction as their most significant challenge in 2023.

2. Vulnerability to AI-specific attacks

As AI customer experiences become more commonplace, malicious actors are slightly behind. A typical example of data poisoning is manipulating or corrupting the data used to train models for machine learning and deep learning. This attack – occasionally called “model poisoning” – attempts to compromise the precision of the AI’s outputs and decision-making.

Similarly, adversarial attacks can threaten customer data operations. They generate datasets that appear in good condition to the naked eye but lead to inaccurate classifications in a machine-learning workflow. Hackers achieve this by launching an assault in the form of fabricated “noise,” leading to an AI/ML misclassification.

Exfiltration attacks aggravate the situation. They may be used to steal training data; for instance, a malicious individual gains access to the dataset and proceeds to misplace, transfer, or siphon off data. Also, as the predictability of gen AI models increases, specific prompts could lead to unintended disclosure of additional information.

3. Compliance and regulatory challenges

AI that receives inputs from individuals with latent biases could produce skewed programming, leading to non-compliance risk and potentially harming your financial performance. For example, when Amazon implemented its AI framework for candidate screening, the algorithm demonstrated a bias towards resumes submitted by male candidates.

In the case of AI customer experiences, think of a chatbot that’s been mainly trained using data supplied by consumers who’ve made high-priced purchases – so that it can respond to product inquiries. The chatbot may offer brief and not-so-helpful descriptions to answer a customer’s inquiry regarding an inexpensive, affordable product.

Discriminatory and biased (whether intentional or not) tech can cause significant harm to a company’s compliance status and financial performance. Moreover, AI has enormous potential for unethical utilization, meaning that organizations might make decisions that expose them to antitrust liabilities.

For instance, if an organization wrongfully chooses to leverage AI to work on pricing decisions, it could disrupt healthy market competition, garnering regulatory scrutiny and possible penalization.

Best Practices for Safeguarding AI Customer Experience Systems

Fortunately, overcoming the challenges around AI cybersecurity isn’t impossible, and by investing in the proper measures, brands can continue to benefit from the power of artificial intelligence in their customer operations.

1. Implement more robust access controls

Businesses must set up role-based access control and user verification processes to prevent unauthorized access to customer records and AI apps. This involves implementing steps to restrict access, such as time-bound passwords, multi-factor authentication, and most minor privilege policies.

2. Encrypt customer data in motion and at rest

Encryption protects data at every stage of its lifecycle – across its transfer to and from the AI application. For instance, TLS and SSL are widely used in transit protocols. To further safeguard data at repose, businesses can implement file encryption or database encryption strategies, including AI training datasets.

3. Adopt confidential cloud computing

Confidential cloud computing can safeguard data even when it’s being processed; this makes it extremely important for AI customer experiences. These safeguards use algorithmic strategies like trusted execution environments and homomorphic encryption to guarantee data safety and privacy, no matter the processing stage.

3. Conduct toxicity detection tests and grounding in your generative AI systems

Toxicity detection is a method by which harmful content, including hate speech and negative stereotypes, can be detected. Using a machine learning (ML) model to analyze and rank the responses supplied by an LLM ensures that any output – regardless of generation – is productive from a business perspective.

Further, by “grounding” the model in actual data and pertinent context, dynamic grounding guides the responses of an LLM with the most recent and precise data. This precludes incorrect responses not founded on reality or fact or “AI hallucinations.”

4. Enforce a strict data retention policy

Organizations must retain customer data for, at most, necessary for BAU customer operations. Utilizing a retention policy for consumer data offsets the risk of unauthorized access and infractions of AI cybersecurity. This ensures compliance with significant data protection laws like GDPR, HIPAA, CCPA, and others while enhancing privacy.

5. Practice masking when formulating AI training datasets

Anonymized data is used instead of sensitive, confidential information during data masking to safeguard private data and conform to statutory regulations. When training AI models, data masking can help determine that all personally identifiable information, like names, phone numbers, and addresses, has been removed. Not only does this aid in AI cybersecurity (by shrinking the payload for potential hackers), but it can also reduce bias.

Building Consumer Trust in AI Systems

It’s unimaginable to believe people were once distrustful of electronic commerce!  Before the widespread adoption of a $1 trillion annual industry, many regular consumers harbored apprehensions about their confidential and financial data safety. There was a trust deficit – an essential, in some ways intangible (but vital) factor for any new idea to find fruition.

Trust will determine the extent to which businesses and consumers successfully embrace the rise of artificial intelligence, specifically generative AI.

Some companies may attempt to transform their CX initiatives without performing the difficult task of eliminating bias, guaranteeing data protection, and offering round-the-clock transparency.

However, this effort will precisely decide how people (your employees or your customers) will believe in the incredible transformative power of AI and earn your organization maximum ROI from AI customer experiences.

Next, read the AWS whitepaper on democratized, operationalized, responsible AI and ML for business leaders. Please click on the social media buttons on top to share this article with your network.