Is my data Truly safe? Truly private? Truly secure?
These questions are keeping today’s business leaders up at night in this dynamic, technologically infused world.
As technology keeps on evolving at a continuous pace, the risk of data exposure has multiplied. Every Enterprise today is racing to adopt modern digital solutions that promise agility, scalability, and growth without losing data. But every new system, every new AI model, and every new integration introduces a new surface area for privacy and security risks.
In less than a decade, artificial intelligence has shifted from experimental pilots to enterprise-critical infrastructure—powering industries at scale. From online banking to e-commerce and healthcare workflows, customer personalization, and decision-making, innovation has never moved faster & smarter.
The Innovation–Privacy Paradox
Rapid digital transformation has shown immense growth, unlocked extraordinary opportunities and unlimited possibilities for varying enterprises—but it has also forced them to realise that the faster we innovate, the harder it becomes to protect data and ensure security.
Modern enterprises have built software that have the ability to process vast volume of information, some of which is sensitive—customer identities, financial data, health records, proprietary code, and trade secrets. With every transaction, every login, and every AI-powered interaction, your data is at risk of leakage, unauthorized access, and misuse making users sceptical of adopting to new applications—making privacy an important check list.
Despite rapid technological advancements, AI at every major innovation, and new age value driven transformations, enterprises often struggle to keep customer data secure and private.
But Where Are Enterprises Falling Short?
While leading enterprises are aggressively deploying AI-driven technologies to bring excellence & intelligence, data privacy and security still is often treated as a secondary consideration, stopping enterprises to invest in enterprise-grade security tools leading to:
- Limited visibility into AI usage and data flows
- Unmonitored access to sensitive data
- Lack of governance across AI pipelines
- Incomplete documentation of AI interactions
These are not minor oversights. They are systemic gaps that often lead to:
- Compliance failures
- Data leakage and unauthorized access
- Misuse of personally identifiable information (PII)
- Exposure of proprietary business assets
In a world governed by guardrails and global regulations such as GDPR, HIPAA, and emerging AI governance frameworks, these gaps can quickly translate into financial penalties and reputational damage.
The root issue is not intent—it’s control and visibility.
According to a recent industry research, 83% of enterprises lack automated AI controls, [source] making it nearly impossible to track how sensitive data is accessed, processed, or shared.
Without end-to-end visibility into AI pipelines, organizations cannot enforce guardrails, ensure compliance, and prove accountability.
And as AI interactions grow more autonomous and complex, manual oversight simply cannot scale.
Why Are Traditional Security Models Failing as AI Matures?
Traditional security frameworks were built for a world of static systems and predictable workflows. They were ethical, effective, and largely chaos-free—well suited for environments where processes were clearly defined and system behaviour was deterministic. However, as AI evolved, these models began to fall behind. Modern AI no longer follows linear paths; it operates in dynamic, often indeterministic environments where workflows continuously shift and redefine themselves.
Today, AI is moving beyond generative capabilities into a far more powerful paradigm: Agentic AI. These intelligent agents don’t just generate outputs—they act, decide, learn, and adapt autonomously, often with minimal human intervention. Their sophistication lies in their ability to:
- Interact dynamically across systems
- Learn and adapt in real time
- Execute actions which humans don’t have clear visibility on
- Generate, access, and consume data continuously without proper consent
This fundamental shift exposes a critical gap in traditional security approaches.
Without agent-specific visibility, policy enforcement, and robust audit trails, enterprises are left in the dark—unable to answer the questions that matter most in an AI-driven ecosystem:
- What data did the agent access—and from where?
- Why did it take a specific action?
- Was the data usage compliant with regulatory and organizational policies?
- Can the agent’s behaviour be explained, audited, or reversed?
These are not theoretical concerns—they are rapidly becoming operational, regulatory, and causing reputational risks.
Traditional AI security frameworks were never designed to govern autonomous decision-making. As Agentic AI reshapes how systems operate, it introduces a new class of risk—one that legacy security frameworks simply cannot address.
And this is where the real challenge begins.
How Is Emerging Agentic AI Creating a New Class of Risk?
The rise of Agentic AI marks a fundamental shift in how enterprises operate and navigate the challenges of data protection.
Unlike traditional AI systems that respond to predefined prompts, Agentic AI systems act autonomously—thinking, planning, deciding, and executing tasks across workflows with minimal or no human intervention. As these agents become embedded into SaaS platforms, enterprises are rapidly deploying them to accelerate decision-making, optimize processes, and reduce operational overheads.
The outcome is magical: faster outcomes, smarter automation, and always-on intelligence leading to maximized growth and revenue.
But this autonomy comes with a new and largely underestimated risk surface.
Agentic AI doesn’t just analyze data and generate information like Generative AI—it can act on it to improve processes & workflows without human knowledge. Not only this, Agentic AI can also retrieve information, interact with systems, trigger actions, and often shares outputs across third party tools and platforms, accidentally exposing sensitive client information without accountability.
And without setting up clear boundaries and strong governance, this autonomy can:
- Access sensitive or restricted data
- Cross data boundaries without context
- Operate beyond consent or regulatory guardrails
- Expose enterprise and customer data to external systems
The risk isn’t that agents exist—it’s that many enterprises are too hasty to deploy them without conformance, sufficient visibility, controls, or auditability.
Use Case: Spotlight on E-Commerce Giants [source]
Agentic AI has already taken root across many diverse industries including e-commerce giants like Amazon who are rapidly using AI-powered agents to maximize their profits and increase customer base by continuously influencing customers using:
- Hyper-personalized product recommendations
- Past purchases and browsing behaviour analysis
- The option of virtual try-ons and “mirror” experiences
- Automated pricing, promotions, and inventory decisions
While these experiences feel seamless and flawless, end users fail to realize that these features heavily rely on continuous access to highly sensitive customer data—including purchase history, preferences, behavioural signals, financial information, and even biometric or visual data.
Here, the challenge is not the agents—it’s the lack of governance around how data is being accessed and used:
- Consent is often implicit, not explicit
- Data boundaries between systems are blurred to avoid penalties & lawsuits
- Sensitive information is reused across contexts
- Compliance checks lag behind real-time AI actions
This creates a perfect storm for privacy violations, regulatory non-compliance, and unintended data exposure—especially in regions governed by GDPR, CCPA, and other emerging AI regulations.
Indium’s Approach to Tackling Modern Data Privacy Problems
In today’s world of Agentic AI and autonomous systems, data privacy has become an important business imperative and a customer satisfaction indicator. As enterprises move forward to deploy intelligent agents across workflows and platforms, the need to protect sensitive data while enabling innovation has become integral.
At Indium, we deeply understand the agentic approach and the new privacy risks it introduces. That’s why we are committed to safeguard personal and other sensitive information of our employees, customers, partners, and other stakeholders which is why data protection is embedded into every fragment of engineering—from design and development to deployment and governance.
Privacy by Design. Security by Default.
Indium’s data privacy strategy is built on the premise of strong governance, technical safeguards, and global compliance. We prioritize:
- Explicit consent and controlled access
- Secure data handling across AI and digital workflows
- Protection of user rights and sensitive PII information
- Compliance with global standards and regulatory frameworks
Our expert engineers operate with one non-negotiable principle: no compromise, no leakage, and no misuse of customer data.
At Indium, data privacy is built into everything we do. We ensure that all personal data is collected, processed, stored, and managed securely—fully aligned with global regulations such as GDPR, HIPAA, SOC 2 Type II, and the Digital Personal Data Protection Act, 2023.
Our approach is guided by responsible data practices including purpose-driven processing, minimal data usage, controlled retention, and strong security controls. With role-based access, encryption, and data masking in place, we protect sensitive information at every step—so our customers can innovate with confidence.
Indium didn’t stop at just crafting policies and maintaining standards, we built platforms that catalysed privacy and took data security to a whole new level.
The Lifter: The Agentic Way of Data Privacy
Indium’s proprietary platform, The Lifter, intelligently analyses millions of lines of code and leverages autonomous agents to modernize complex legacy applications—transforming fragmented operations into future-ready, intelligent architectures.
At its core, The Lifter enables enterprises to gain control, visibility, and trust in their data by:
- Centralizing and streamlining data operations across legacy systems and modern platforms
- Eliminating blind spots caused by data silos and inconsistent governance
- Ensuring data remains accurate, compliant, and resilient across its lifecycle
- Enabling AI-driven insights while maintaining strict privacy and security controls
- Detecting hidden data anomalies and unusual workflow patterns
- Flagging potential compliance risks in real time
- Supporting adherence to regulations such as GDPR and HIPAA
This proactive monitoring helps enterprises stay ahead of breaches, misuse, and non-compliance.
To further strengthen data protection, Indium embeds privacy-first engineering practices across development, testing, and production workflows, including:
- Data masking and de-identification of personally identifiable information
- Pseudonymization and scrubbing of sensitive information
- Cryptographic safeguards designed for modern, high-scale systems
These measures ensure sensitive data is never exposed—whether during development, testing, or production.
The Lifter also addresses the challenge of over personalization and deep data simulation caused due to multi-agent orchestration using Blackbox, Indium’s proprietary privacy-first agent. this challenge head-on with
Blackbox ensures complete data sovereignty. All sensitive customer information remains securely within the customer’s own environment—never exposed, never shared, and never compromised. This on-premises safeguard eliminates the risk of data leakage while enabling The Lifter to operate at full intelligence.
Beyond Frameworks: How Indium Ensures End-to-End Data Protection
While Indium’s proprietary frameworks form the foundation of privacy, true data security goes beyond frameworks or platforms. Indium strengthens data governance by combining industry-leading platforms, AI accelerators, and proven data-protection techniques—all working together harmoniously to safeguard sensitive information across modernization initiatives.
To enforce such policies & guardrails and automate compliance at scale, Indium integrates trusted governance and data platforms, including:
- Collibra and Alation for data governance, cataloguing, and policy enforcement
- Databricks for secure, governed data processing in modern data environments
- Kerberos for role-based, authenticated access control across big-data ecosystems
Together, these tools ensure that the right users access the right data—securely and compliantly.
How Indium Leads the Industry with Trust and Integrity
Many organizations are now prioritizing data privacy in response to shifting customer expectations. Indium, however, has always led with integrity by design. Here’s what sets us apart:
- Blockchain-Based Audit Trails
Every data interaction is traceable and verifiable, enabling transparent governance and accountability.
- AI-Driven Anomaly Detection
The Lifter continuously monitors data activity to detect and flag unusual behaviour in real time.
- Quantum-Safe Encryption Testing
Encryption mechanisms are tested against future-ready threat scenarios to ensure long-term resilience.
- Real-Time Compliance Monitoring
Live policy tracking ensures compliance is maintained even as systems scale rapidly.
At Indium, data privacy isn’t a reaction—it’s a responsibility. By combining robust governance, AI-driven intelligence, modern techniques, and advanced security practices, we help enterprises across the globe modernize with confidence—without ever compromising on trust.
