Vinay is the CEO and co-founder of Wald.ai, a pioneer in data security for Gen AI. Vinay was CPTO at Kiavi, a leading Fintech in the mortgage lending space. As Chief Digital Officer at JLL, a Fortune 150 company, he led and scaled a global product development organization. Prior to JLL, he spent over a decade at Google leading product teams across Google Maps and Enterprise. He has a strong background in internet security after leading products at companies like CheckPoint Software and Webroot.
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AI has become part of daily work. Employees use it to draft emails, write code, analyze documents, and speed up routine tasks. Productivity is improving, but something else is happening quietly in the background. Sensitive data is slipping into systems that were never built to protect it.
This is not hypothetical. It is real, it is happening today, and it is dangerous. Every time an employee pastes a customer record, a financial detail, or even a snippet of source code into an AI tool, the company’s risk exposure grows. The intent may be harmless, but the outcome can be severe.
Enterprises carry large volumes of personally identifiable information (PII) and regulated data. Compliance with GDPR, HIPAA, PCI-DSS, SOC 2, or CCPA is not optional. Regulators do not accept “we did not know.” Boards cannot excuse reputational damage. Customers will not forgive carelessness.
The truth is that most AI tools were not designed for compliance. They were created to generate answers, to accelerate work, and to feel intuitive. They are powerful, but they are not secure by default. When employees put sensitive information into them, the company inherits risks it cannot see and cannot control.
Here is the reality. Employees will use AI. They will use it whether policies allow it or not. It is too fast, too convenient, and too effective to ignore. Writing memos or trying to ban AI is not a strategy. It is wishful thinking.
The question for leadership is not “will employees use AI?” They already do. The real question is “how do we see and control what happens when they use it?”
Sensitive data does not always look like a credit card number or a Social Security ID. Sometimes it is the structure of a contract, a client proposal, or an internal strategy document. Context makes it sensitive. Detecting that requires more than pattern matching. It requires intelligence that understands meaning.
At Wald.ai, we built our DLP platform for exactly this challenge. Traditional systems look for fixed identifiers. Ours looks at context and intent. That difference changes how enterprises stay safe.
When an employee uses ChatGPT, Claude, or Gemini, Wald.ai works in real time. It sees what information is leaving. It recognizes sensitivity even when obvious markers are missing. It gives leaders visibility without slowing employees down. Compliance is protected, and productivity continues. Security becomes a driver of trust, not an obstacle to progress.
Keeping sensitive data safe in an AI driven workplace is not tomorrow’s challenge. It is today’s responsibility. Leaders who wait will explain breaches. Leaders who act will protect customers, employees, and investors.
The steps are clear. Accept that employees will use AI. Recognize that sensitive data will reach those tools unless controls are in place. Invest in solutions that understand both context and intent. Treat security as a culture, not just a checkbox.
The companies that move first will not only avoid fines and headlines. They will build trust, move faster, and create a foundation for innovation. At Wald.ai, we believe that is the only sustainable way forward.
ChatGPT is powerful, no doubt. But for businesses that live and breathe sensitive information, the question is less about capability and more about safety. AI is reshaping entire industries—PwC says it could add $15.7 trillion to the global economy by 2030. That’s massive. But growth this big always carries risk.
We’ve already seen warning signs. Italy’s data protection authority flagged privacy issues. Samsung had employees accidentally leak confidential data. Children’s Hospital Colorado paid a $548,265 HIPAA fine after breaches. These aren’t small stories—they’re flashing red lights for any organization that has to comply with GDPR, HIPAA, or similar regulations.
Here’s the thing: your choice of ChatGPT deployment—public or private—directly shapes your risk profile. Let’s break it down.
The single biggest question businesses should ask: where does my data actually go?
When you use the public app, every prompt, every file, every response goes to OpenAI servers. Their policy admits: “we may use content submitted to ChatGPT to improve model performance.” That means your data might travel across systems in the US and elsewhere. And yes, a “limited number of OpenAI personnel” may access it. If you’re handling sensitive data, that’s a serious exposure.
Private deployments—on-prem or within a Virtual Private Cloud (VPC)—keep data inside your walls. Nothing leaves unless you allow it. You control the configuration, storage, and policies. For industries that simply cannot risk leaks, this control is critical.
Regardless of where you run ChatGPT, you need guardrails:
Public ChatGPT keeps conversations for 30 days by default—even after deletion. With private deployments, you set the retention clock, not OpenAI. There are multiple ChatGPT Alternatives that provide ability to set a retention policy.
Let’s be clear: no system is bulletproof. Public ChatGPT has had its moments—remember the March 2023 bug where users saw other people’s chat history? Or CVE-2024-27564, the exploit that redirected users to malicious sites, with 10,000+ attempts in one week?
OpenAI invests heavily in security: third-party pen tests, a bug bounty program, and ongoing patches. But a public platform is still a bigger target. Private deployments reduce the attack surface by keeping your AI isolated inside your infrastructure.
The question isn’t “is ChatGPT secure?” It’s “is it secure enough for my industry and risk tolerance?”
Compliance is where things get serious. A single slip here isn’t just a security failure—it’s a regulatory nightmare.
Standard ChatGPT is not HIPAA-compliant. Period. OpenAI itself warns against sharing sensitive data in the free or Plus versions. Only enterprise offerings (ChatGPT Enterprise, Team, Edu, API) provide DPAs for GDPR and BAAs for HIPAA. And those protections still rely on you trusting OpenAI’s servers.
Private deployments, on the other hand, let you enforce custom compliance rules directly: HIPAA guardrails, GDPR data minimization, CCPA opt-outs—on your terms.
Enterprise-grade AI isn’t just about encryption. It’s about control. Private setups let you define exactly who sees what, implement least-privilege access, and track every action with immutable audit logs. Public ChatGPT? Limited controls.
OpenAI’s enterprise products have SOC 2 Type 2, GDPR, and CCPA certifications. Solid, but not customizable. Private deployments can be designed to meet specific industry frameworks like ISO 27001 or custom audit requirements.
AI is not one-size-fits-all. The way you guide and integrate ChatGPT changes the value you get from it.
Private AI isn’t cheap. A decent self-hosted setup with Llama 3 or Mistral could run $4,000–$30,000 upfront plus power costs. Compare that with the pay-as-you-go pricing of OpenAI’s API.
But here’s the nuance: at scale, private wins. A 13B parameter model can be 9x cheaper to run than GPT-4 Turbo if you’re using it heavily. For startups? Public API is the economical play. For enterprises with constant usage? Private pays for itself.
If your business handles sensitive data, public ChatGPT isn’t enough. It’s convenient, affordable, and powerful, yes—but risky. Private deployments demand higher upfront costs but deliver what enterprises need: control, compliance, and security.
Healthcare and finance? Go private. Retail and HR? Public may be enough. Most organizations will likely end up with a hybrid approach.
The bottom line: AI offers massive upside, but security and compliance are non-negotiable. Choose the setup that protects your data before it’s too late.
Q1. Is ChatGPT safe for sensitive business data?
Not by default. Public ChatGPT sends everything to OpenAI servers. Private deployments give you better control and reduce risks.
Q2. What’s the key difference between public and private deployments?
Public relies on OpenAI’s infrastructure. Private runs in your environment, giving you control over storage, access, and compliance.
Q3. Is ChatGPT HIPAA or GDPR compliant?
Public ChatGPT isn’t HIPAA compliant. Enterprise tiers improve compliance, but private deployments remain the safest path for regulated industries.
Q4. How do costs compare?
Public API is cheaper upfront. Private hosting costs more initially but can be significantly cheaper at high usage volumes.
Q5. Can ChatGPT be customized?
Yes. Private deployments allow fine-tuning, deep system prompts, and integration with internal tools—something public ChatGPT can’t match at scale.
AI assistants are everywhere now. In sales. In operations. In compliance workflows. They’re fast, flexible, and transformative. But here’s the problem: every prompt is also an opening. Every response is a potential leak. And attackers know it.
That’s why Gen AI security is no longer optional. It’s essential. And at the heart of it sits one practice that too many enterprises overlook: data sanitization.
Here is a list of ChatGPT breaches that have happened in the past.
Think of data sanitization as the first security checkpoint. Before information even touches an AI system, it gets validated, filtered, and scrubbed. Bad inputs never make it through. Sensitive details get neutralized. The attack surface shrinks dramatically.
The impact is measurable. Organizations with strong sanitization protocols see 76 percent fewer AI-related security incidents. That’s not theory. That’s reality.
Without sanitization, enterprises face more than breaches. They deal with biased outputs, compliance failures, and reputational hits that take years to repair. With it, they gain reliable performance, consistent insights, and a security posture built for scale.
Here’s what often gets missed: sanitization doesn’t just protect. It improves AI. Clean data makes models sharper. It reduces drift. It strengthens the trust between humans and machines.
So when leaders talk about Gen AI security, they should be talking about more than firewalls or endpoint protection. They should be asking: “Are we feeding our AI the kind of data that keeps us safe and accurate at the same time?”
At Wald.ai, we see the consequences of skipping this step. Thousands of sensitive data points pass through AI assistants every month inside an average enterprise. Without sanitization, those data points are exposed. With sanitization, they are protected before they can ever leak.
Our approach is built for real-time defense. Contextual filtering keeps meaning intact while scrubbing the risk. Custom rules adapt to industry regulations like HIPAA, GDPR, and CCPA. Encryption and retention controls let enterprises keep ownership of their data. And continuous monitoring ensures nothing slips through unnoticed.
The result: confidence. Enterprises deploy AI assistants without fearing that every prompt could become a headline.
The smartest organizations treat data sanitization as strategy, not as a patch. Some of the practices we see working best include:
None of these are new on their own. But together they form the architecture of modern Gen AI security.
The future of data sanitization will be even smarter. Expect AI systems that automatically adapt to new attack vectors. Immutable audit trails backed by blockchain. Encryption designed specifically for AI-processed data.
Security leaders who act now will be positioned to absorb these advances seamlessly. Those who wait will spend years catching up.
The truth is simple. There is no Gen AI security without data sanitization. Not partial protection. Not good-enough defenses. True, scalable, enterprise-ready security begins with clean, controlled, and trusted data.
Leaders have a choice. Ignore sanitization and hope for the best, or treat it as the cornerstone of AI security and build systems that employees and regulators can trust. The enterprises that choose the latter will be the ones that harness AI’s full potential without sacrificing safety.
Imagine this: you’re swamped at work and need to draft a quick email about a confidential project. Instead of typing it yourself, you turn to a large language model (LLM) like ChatGPT or Gemini. These AI whiz-kids can whip up emails, analyze documents, and even write code in seconds – a real time-saver! But here’s the rub: traditional data leakage protection (DLP) might not be keeping up with this new way of working.
Why? Because traditional DLP relies on old-school methods like data fingerprinting and regular expression matching. These techniques are great for catching things like credit card numbers or employee IDs bouncing around in emails. But they’re not so good at sniffing out leaks happening in a whole new world: prompts sent to LLMs.
Data fingerprinting works by creating a unique digital signature for sensitive data. But what if the data leak isn’t a copy-paste job? Users can inadvertently paraphrase, rephrase, and even introduce never seen before information in their prompts. Traditional DLP might miss these leaks.
Regular expressions are like search filters for specific patterns in text. They’re helpful for spotting basic leaks, but they can’t understand the context of an LLM prompt. Imagine a prompt asking about “Project X,” a secret initiative. A basic filter might miss it, leaving your sensitive data vulnerable.
Traditional DLP focuses on what data is being sent, not why. But with LLMs, the intent behind a prompt is crucial. A seemingly harmless prompt about “financial data” could end up leaking confidential information. Traditional DLP might not pick up on this.
So, what are we supposed to do? Throw out our DLP altogether? Absolutely not! DLP is still essential for protecting other forms of data leaks. But we need to level it up for the LLM era.
New DLP solutions need to understand the context of prompts sent to LLMs. This might involve analyzing the prompt to identify potential risks and then using data anonymization techniques to mask confidential data.
Imagine a DLP system that can not only analyze text but also consider the intent of the prompt. Sensitive topics when leaked can create HR and legal nightmares for companies. These prompts may not contain confidential data but have potent intent and when leaked can cause irreparable harm.
LLMs are constantly evolving, and so should DLP. The ideal solution should be able to adapt to new ways LLMs are used and identify emerging security threats.
LLMs are powerful tools that can revolutionize the way we work. However, traditional DLP needs an upgrade to keep pace with this evolving technology. By focusing on context, user intent, and continuous learning, we can build a new generation of DLP that protects sensitive data in the age of LLMs. Remember, data security is an ongoing journey, not a destination. By embracing these advancements, we can ensure that LLMs empower our work without compromising our information security.