In today's dynamic digital landscape and complex regulations, achieving robust data security and privacy hinges on the teamwork of dedicated and skilled professionals. This is an exciting opportunity for tech admirers who are passionate about safeguarding information, as their expertise is vital for organizational success. With the ever-changing insider threats and regulations, these specialists are at the forefront, inspiring innovation and paving the way for secure, compliant enterprises that thrive in the future!.
In an exclusive interview, Mr. Arfi Siddik Mollashaik, a Solution Architect at Securiti.ai, and Mr. Carl Williams, a senior tech journalist at TechTimes, explored the transformative potential of AI and machine learning (ML) and their anticipated impact on data security and privacy.
1. What motivated your research into AI-driven data classification frameworks?
Mollashaik: The exponential increase in sensitive data generation in the form of structured and unstructured data within an organization makes it very challenging to classify the data. The foremost step is to classify the data according to regulations and protect the data from insider threats. Traditional classification techniques use the concept called data domains. These data domains internally use rule-based regular expressions and run against the data and metadata. These rule-based classifications always result in more false positives and negatives, and the accuracy is between 60% and 70%. The sophistication of insider threats made it clear that traditional rule-based security systems were becoming insufficient.
I recognized the need to transition towards dynamic, AI-powered frameworks capable of proactive threat detection, adaptive risk response, and robust privacy preservation mechanisms. AI introduces advanced machine learning algorithms that can process massive data streams in real-time, learning to recognize multiple data and metadata patterns. This allows systems to detect and classify the data faster and more accurately than ever before. I started implementing AI and ML on top of the rule-based algorithms using content and contextual properties of the data, resulting in 95% accuracy in the data classification. This helped me implement a robust security and privacy solution.
2. What is the critical role of data classification taxonomies in privacy protection, and how have you used them in real-time at the Fortune 500 customers you worked with?
Mollashaik: Robust data classification frameworks help contextualize data sensitivity, regulatory obligations, and potential privacy risks. These hierarchies are foundational to implementing precise access controls and adaptive privacy-preserving policies. When I was working for one of the largest airlines in the U.S.A., I realized that their privacy program was struggling with manual data classification and identifying data subject details, and failing to respond to the data subject on time, which violates the GDPR and CCPA regulations. I designed a solution that used AI & ML technology to classify the data across hundreds of both structured and unstructured data systems with petabytes of data.
Once we have data classified, then I tuned the process to only retrieve the data based on the classifications, and thus helped the organization to respond to data subjects with their rights on time, which is 30 days after the submission of the request. Success requires phased integration, careful architecture planning, and continuous monitoring. These quality data classifications enabled me to implement enterprise persistence and dynamic data masking solutions in the healthcare and financial sectors. Practical implementations improved data classification efficiency by 95%, reduced compliance incidents by 85%, and dramatically improved regulatory compliance.
3. What challenges do organizations face when integrating AI with privacy and compliance requirements?
Mollashaik: The primary challenge is performance optimization at scale. Many systems lack the computing capacity to run advanced models in real time. There's also the need to ensure explainability and trust in AI decisions, especially in regulated sectors. Overcoming these hurdles requires architectural innovation and strategic policy alignment. Industries like healthcare, finance, and insurance that process large volumes of regulated data will gain the most. My framework supports context-aware access control and compliance with GDPR, HIPAA, and PCI DSS. It allows organizations to automate security without compromising efficiency or violating privacy laws.
4. In your view, what steps should companies take to ensure they are genuinely respecting user privacy, rather than just finding ways around the rules?
Mollashaik: The difference between true privacy leadership and mere compliance comes down to intent and transparency. Companies should focus on building clear, ethical data practices—ensuring that user consent is meaningful, that data use is transparent, and anonymization protects individual identities. This means collaborating closely with legal and technical teams, reviewing data flows rigorously, and designing privacy into products by default. Ultimately, the goal should be to uphold the spirit of privacy laws, not just the letter, to earn and preserve user trust as the foundation of business success.
5. How can organizations find a practical balance between strong security and robust privacy protection in their AI deployments?
Mollashaik: The balance starts with transparency and data minimization—organizations must clearly define what data is essential for security functions and avoid collecting anything excessive. Security leaders should work closely with privacy and legal teams to ensure that safeguards are built into AI systems from the outset. This includes giving users clear notice about data practices, implementing technical controls such as anonymization or pseudonymization, and regularly auditing AI models for bias or privacy overreach. Ultimately, the goal should be to achieve strong protection without compromising user trust—proving that it's possible to build resilient systems that respect individual privacy rights even in the age of intelligent, data-driven cybersecurity.
6. What are the main differences between traditional information security practices and a privacy-focused approach, and why is it essential for organizations to distinguish between the two when managing personal data?
Mollashaik: I started my journey as a data security architect and moved to privacy engineering. The move from security to privacy is less about new technologies and more about adopting a mission of purpose-driven, rights-based data governance. Privacy professionals must ensure data is collected and used transparently, purposefully, and legally, working across teams (including legal) and educating others about the subtle but fundamental shift from securing data to respecting and protecting data subject rights.
Key lessons I learned while transitioning from Security to Privacy:
- Redefining PII (Personally Identifiable Information): Security teams often focus on classic identifiers like Social Security or credit card numbers. In privacy programs, PII covers any data that can identify an individual, including data combinations and less obvious fields. Success requires collaborative understanding across teams so everyone knows what constitutes PII.
- Purpose Over Protection: Security professionals instinctively prioritize encryption, access control, and system hardening for data protection. Privacy demands asking why data is stored, its intended use, and whether proper consent and notification have been given. If data isn't needed for a clear purpose or retention is unjustified, it should be deleted—even if technically secure.
- Review Applications and Data Retention: Teams sometimes believe robust security controls are equivalent to privacy compliance. However, privacy is breached if data is retained or shared beyond its intended purpose, even if secured. Proactive, policy-driven review of data stores, retention, and minimization is necessary.
- Legal Collaboration: Privacy regulations are complex and jurisdiction-specific, requiring close partnership with legal teams. Legal experts help define compliant collection, usage, cross-border transfer, and communication policies. Bringing legal in from the start ensures privacy frameworks meet evolving regulatory standards.
- Process Evaluation and Mindset Shift: Data flows across teams and organizations, which means privacy must be managed at each stage. It's critical to question not just how data is protected but why it is collected, shared, and accessed. Security and privacy are often conflated; success in privacy requires educating teams about their differences and the importance of transparency and purpose.
- Personal Adaptation and Education: Transitioning to privacy leadership entails being both a teacher and a student, learning regulatory nuances, and explaining privacy concepts to technical teams. It means embracing a new mindset—privacy is about user autonomy and lawful, transparent data use, not just defense against breaches.
7. Where do you see the most significant future opportunities for AI in data security?
Mollashaik: I see quantum-resistant cryptography, federated learning, and zero-trust architecture as transformative trends. As quantum computing advances, protecting encrypted data and building collaborative, privacy-aware AI systems will become paramount. I expect continuous convergence of privacy, security, and regulatory compliance, driven by AI and automation. Organizations will increasingly rely on AI-enhanced adaptive frameworks that can respond to both current and quantum-era threats, while upholding the highest privacy standards. Once GDPR was introduced, other global regulations quickly followed, compelling organizations to make privacy a foundational design principle.
Now, AI-driven frameworks must natively support data minimization, transparency, right-of-access, and robust auditability to ensure compliance. Significant advances include federated learning, differential privacy techniques, and homomorphic encryption, which enable organizations to build AI systems that learn from distributed data sources while minimizing exposure of sensitive information and maintaining utility.
8. What's your message to enterprises looking to adopt intelligent data security systems?
Mollashaik: Security isn't just a function—it's a culture. As threats grow more sophisticated, systems must evolve to be predictive, contextual, and adaptive. AI-based security frameworks represent that future. Organizations must act now to integrate these tools into their architecture, or risk being left behind. Protecting privacy, mitigating algorithmic bias, and ensuring transparency in AI decision-making—especially when resources are constrained in edge environments—are ethical imperatives. Ensuring explainability builds trust and accountability. Successful implementation of data security and privacy requires capable, disciplined teams. This creates a valuable opportunity for technology experts in these fields to significantly contribute to their company's success.
"The views expressed in this article reflect independent contributions to industry-wide governance and do not reflect the views of any specific employer."
ⓒ 2025 TECHTIMES.com All rights reserved. Do not reproduce without permission.
