Keeping Customer Data More Secure with AI
Customer data theft and cyberattacks are escalating rapidly, with data breaches increasing by up to 40 percent globally this year, according to the latest statistics from SentinelOne, a cybersecurity solutions provider. This surge represents a 70 percent increase in weekly cyberattack volume since 2023 and an 18 percent increase in just the past few months alone.
In many of these incidents, attackers are increasingly using automated, agentic artificial intelligence to conduct reconnaissance and exploit vulnerabilities at machine speeds.
To try to stay ahead of fraudsters and guard against these data breaches, companies also need to turn to AI.
“AI is becoming one of the strongest tools we have to keep customer data secure,” says Sean Hauver, chief information officer of Alorica, a customer service experience outsourcing services provider. “Modern organizations generate an enormous volume of signals that no human team could realistically monitor. AI fills that gap by recognizing patterns, detecting anomalies, and surfacing threats in real time, well before they become breaches.”
Hauver adds that AI can help with the governance of customer data and other sensitive material through automated content and policy moderation: “Models can detect exposed [personally identifiable information], risky uploads, harmful documents, or malicious messages in seconds. I’ve seen these systems work in industries like banking, travel, and tech to prevent fake loan applications, block bot-driven scams, filter fraudulent listings, and stop toxic profiles before they cause harm. The impact is faster detection, fewer mistakes, and dramatically lower exposure to human error.”
Hauver also maintains that AI can protect data with robust behavioral analysis. Modern threat patterns rarely appear as obvious violations; they show up as subtle anomalies, such as unusual access patterns, abnormal typing cadence, suspicious login behavior, or coordinated account activity, he says, noting that AI models can learn what’s normal across millions of interactions and flag deviations instantly.
Alorica uses AI to catch identity theft attempts, fraudulent transactions, impersonation, and multi-account abuse before it impacts customers, according to Hauver
AI can also assist in adaptive identity verification, Hauver continues. Instead of relying solely on passwords or static questions, AI can layer voice biometrics, behavioral signals, device intelligence, and contextual location data to authenticate users with far greater accuracy, reducing account takeovers without increasing friction for legitimate customers, he says.
AI is infiltrating every aspect of CX, with the technology quickly becoming a frontline defense for protecting customer data, according to Seth Johnson, chief technology officer of Cyara, a customer experience testing solutions provider. “The organizations getting this right are using AI intentionally to assure secure, compliant, and trustworthy customer journeys end to end,” he said.
“AI can absolutely strengthen customer data security if used with discipline, not convenience,” adds attorney Michael McCready, managing partner of McCready Law in Chicago.
“There’s something ironic about all the panic around AI and data security,” acknowledges Tim Heaton, data governance manager at Renasant Bank, a regional financial institution based in Tupelo, Miss., with branches throughout the American Southeast. “The same thing people are worried will leak customer data can actually be one of the better tools we’ve got to protect it.”
One of the most immediate ways AI improves data security is through real-time monitoring and validation of customer interactions, Johnson adds. Organizations can train AI to detect when sensitive data is being requested, shared, or mishandled during a conversation, whether it’s a chatbot collecting unnecessary personal information or generating responses that expose regulated data. This type of CX assurance allows organizations to flag and stop risky interactions before they escalate into compliance violations.
AI can also help control data access, according to Shlomi Beer, cofounder and CEO of ImpersonAlly.io, a cybersecurity firm. “Instead of giving static permissions, AI allows companies to move toward a full dynamic access, meaning employees only get access to the data they actually need, when they need it. In practice, most employees don’t need full or even partial access to customer data all the time, but historically that’s how systems were set up, because settings could not be dynamically monitored and changed.”
With AI, behavior can be evaluated on a case-by-case basis: what someone is working on, what they usually access, and whether a specific request makes sense in context, helping to reduce unnecessary exposure of sensitive data, Beer says.
Jason Mann, founder of growth marketing firm STOCK, stresses the speed with which AI can mitigate threats: “AI is great at threat detection, analyzing network behavior in real time, flagging anomalies before a breach even occurs, and then identifying the attack patterns and reacting almost immediately to potential attacks, where it would’ve taken a human analyst much more time to recognize and react,” he says.
Companies that want a higher level of security can deploy enterprise-grade AI security platforms that operate under a zero data retention policy and include operating and data processing agreements that guarantee data will not be used to train the model or anyone else’s model, Mann continues. “This level of security is the priciest but the most secure option for handling customer data security with AI assistance.”
But still, Mann and many others argue that companies should not rely exclusively on AI to safeguard their data. “AI is not a replacement to fundamental security measures such as encryption, firewalls, network segmentation, and [multifactor authentication],” Mann states.
“As organizations increasingly adopt AI to improve efficiency and customer experience, protecting customer data must remain a first-order priority,” adds Colton De Vos, marketing and communications specialist at Resolute Technology Solutions, a managed IT support services company. “Internally, our approach has been to treat AI not as a special exception but as another data-processing system that must meet the same or higher security and governance standards as any other technology.
“Our incident response plan reinforces that having clear detection, escalation, and response procedures reduces both the impact of security incidents and the loss of customer trust when something goes wrong,” De Vos continues. “AI can enhance this by identifying anomalies, but it must operate within a well-defined response framework.”
The problem with AI to combat data risk is that “a lot of folks are still treating it like a side project instead of something that needs real structure,” Heaton adds.
Guardrails Needed
And just as with any other use case for AI, data protection with the technology requires robust guardrails that keep the technology from running completely amok, experts agree.
“The biggest mistake I see organizations make is treating AI like a shortcut, instead of a system that requires the right guardrails,” McCready states.
“Most organizations deploy AI on top of customer data without understanding what it is doing with it and without putting in any serious guardrails around it. That’s the core issue,” adds Diptamay Sanyal, principal engineer at cybersecurity firm CrowdStrike.
Among the recommendations is control over the data flow. Not all data belongs in an AI tool, according to McCready, who notes that sensitive customer information should never be casually pasted into public or unsecured models.
“The best practice is to use enterprise-grade AI platforms with clear data-handling policies, or better yet, deploy internal tools trained on your own controlled environment. If you don’t know where your data is going, you shouldn’t be putting it there.”
Similarly, De Vos recommends data classification before AI use, pointing out that there have been many real-world incidents where sensitive but unclassified data was uploaded into public AI tools, triggering security alerts and internal investigations. Treating AI like any other system that processes data rather than a shortcut tool helps prevent accidental data leakage.
And just as important as it is to control data flow, it’s also important to control data access. AI amplifies human risk if you’re not careful, McCready cautions. “Limit who in your organization can use AI tools, define clear use cases, and train your team on what’s acceptable. Most data breaches happen because of simple human mistakes, not sophisticated hacks.”
Edward Tian, founder and CEO of GPTZero, developers of AI detection software that analyzes text to determine if it was likely generated by large language models, adds that segmentation of environments is another important guardrail. “By keeping environments separate from each other, especially when dealing with sensitive customer data, you can mitigate the risk of a single point of failure from taking down all of your environments. This becomes even more essential as companies start to introduce agents or automated workflows.”
Another suggested guardrail is building verification into any AI-related data workflow: AI is excellent at summarizing, organizing, and accelerating tasks, but it should never be the final decision maker when customer data is involved. Human review isn’t optional. It’s the safeguard. Accuracy, confidentiality and judgment still sit with people.
AI can identify anomalies and enforce policies across an enterprise, but humans need to be involved in the security process as well, experts agree.
The most overlooked best practice in protecting customer data is keeping humans in command, Hauver maintains. “The strongest systems pair AI’s pattern recognition with human oversight, audit trails, and escalation paths. AI raises the flag; humans make the final call. When you get the balance right, AI builds trust, reduces risk, and strengthens the entire customer experience.”
McCready urges treating AI like a powerful employee that is fast and capable but requires oversight. “Used correctly, it can actually reduce risk by organizing data, flagging anomalies, and tightening workflows. Used carelessly, it does the opposite.”
Ground It with Governance
Experts can’t overemphasize the importance of data governance, urging companies to think long-term about it. AI should plug into a broader security strategy that includes encryption, secure storage, audit trails, and regular compliance reviews, they say, noting that if AI use isn’t documented and monitored, companies are creating dangerous blind spots.
“AI is already being used to protect customer data in ways that far outpace most companies’ ability to build the governance structures to oversee it,” says Chris Hutchins, founder and CEO of Hutchins Data Strategy Consulting. “This gap creates vulnerabilities that technical controls alone cannot address.”
Leading businesses are using AI tools integrated into a more comprehensive governance structure instead of viewing AI as their governance framework, Hutchison adds. “That difference is especially important when things go wrong.”
Vendor risk is often overlooked when deploying AI for customer data and for other uses, Hutchins cautions. “Numerous businesses utilize AI technologies that collect customer data seamlessly integrated into their workflows without fully comprehending the downstream implications of that data, whether that data is used for model training, and which contractual protections define that data. That evaluation is a baseline requirement, not a matter of discretion.”
Logging definitely needs a second look, according to Heaton, who recommends capturing prompts, responses, and who asked for them, then actually watching for patterns that don’t look right.
Heaton also cautions against the notion that AI can be a set-it-and-forget-it technology, especially when it comes to something as critical as data security. “Things change. Models evolve. What felt safe a few months ago might not be today. The teams getting this right are constantly checking, adjusting, and sometimes hitting the brakes,” he states.
Sanyal agrees: “The thing most teams still get wrong is thinking AI security is a setup task. It isn’t. Models get updated. Prompts drift. Someone adds a new integration. If we’re not actively watching what our AI is doing in production, we genuinely don’t know what’s happening to our customer data. That’s a problem.”
And even when companies follow all of the recommended best practices, compromises of customer data can still occur, De Vos cautions. So he recommends deploying AI with robust monitoring and incident response readiness.
Phillip Britt is a freelance writer based in the Chicago area. He can be reached at spenterprises1@comcast.net.
AI Security Best Practices
Leigh Segall, CEO of Smart Communications, has observed that organizations that keep customer data most secure with artificial intelligence share the following three best practices:
- They maintain a single, governed source of truth. When AI draws from multiple disconnected systems, inconsistencies multiply, and so does risk.
- They build auditability in from the start rather than retrofitting it later.Every AI-assisted action should leave a trail that can be reconstructed and explained. Organizations that treat this as an afterthought tend to discover its importance at the worst possible moment.
- They keep humans in the loop at the moments of highest consequence.AI should support decisions, not replace accountability. In industries where a single miscommunication can carry severe legal, financial, or health consequences, human oversight is ultimately what makes AI safe to deploy at scale.