Operationalizing Trust in Agentic AI: How Cognizant and BigID Are Shaping the Future of Safe, Scalable AI

As agentic AI moves from experimental pilots to enterprise-wide deployment, organizations face a fundamental challenge: how to govern autonomous systems that reason, act, and make decisions across data, models, workflows, and third-party ecosystems.

According to Gartner, “Forty percent of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% today.” This shift promises dramatic gains in productivity and collaboration—but it also introduces new forms of risk. Many enterprises are attempting to govern agentic AI with controls designed for static applications, creating blind spots around data usage, accountability, and compliance.

To scale agentic AI safely, governance, privacy, and security must move from policy documents into operational reality.

The Governance Imperative for Agentic AI

Agentic AI systems operate with increasing autonomy, often interacting with sensitive or regulated data in real time. This raises critical questions around lawful data usage, consent, accountability, and compliance with regulations such as GDPR, CCPA/CPRA, India’s Digital Personal Data Protection Act (DPDP), and emerging AI-specific laws.

To operationalize governance for agentic AI, enterprises need capabilities that go well beyond traditional controls:

• End-to-end data visibility and lineage to understand what data powers AI agents, where it originates, and how it flows across models and vendors
• Consent- and policy-aware AI pipelines that enforce usage restrictions dynamically at prompt time and during model execution
• Automated compliance operations, including DSAR fulfillment across unstructured data, vector databases, and model memory
• Continuous risk, drift, and bias monitoring to detect misconfigurations, unauthorized access, and harmful outcomes before they escalate

Governance must be embedded into the AI lifecycle itself—not bolted on after deployment.

Cognizant & BigID: Turning Responsible AI from Principle into Practice

Cognizant and BigID address this challenge from complementary angles—together bridging the gap between AI governance intent and real-time enforcement.

Cognizant’s Responsible AI Trust Framework spans the full AI lifecycle, from governance and risk assessment to solution design and post-deployment oversight. It is supported by a modular Trust Platform that enables explainability, testing, and agentic monitoring. Notably, Cognizant is the first global IT services provider accredited to ISO/IEC 42001:2023, the international standard for AI management systems.

Cognizant’s approach emphasizes:

• Audit-ready governance aligned with ISO, NIST, and OECD standards
• Human-led oversight, including escalation protocols, red-teaming, and incident response playbooks
• Ethical risk assessments to evaluate potential harms and unintended consequences before launch
• Cross-functional engagement models that bring together legal, privacy, risk, data science, and business stakeholders

Where Cognizant defines how AI should be governed, BigID delivers the data visibility and control layer required to make governance actionable at scale.

BigID’s Data-Centric Governance for Agentic AI

BigID gives organizations the discovery and enforcement they need to govern AI — from managing shadow AI to securing training data pipelines and ensuring responsible use.

Key capabilities include:

Secure and Trusted AI Inputs

• AI Data Cleansing to redact, tokenize, or replace sensitive data before it enters AI pipelines
• AI Data Labeling and Trust to classify and validate data by sensitivity, consent, lineage, and policy status

Control AI Usage

• Prompt Protection to detect and redact sensitive data during AI interactions without disrupting user experience
• Employee and Agent Access Controls to enforce guardrails on how copilots, agents, and LLMs can access data

Detect and Manage AI Risk

• Shadow AI Discovery to identify unsanctioned models, agents, copilots, and tools—and trace the sensitive data they touch
• AI Security Posture Management (SPM) to monitor configuration risk, anomalies, and data exposure across AI systems

Enforce Governance at Scale

• Automated AI Risk Assessments aligned to NIST AI RMF, ISO 42001, and internal policies
• Remediation and Enforcement Actions to restrict access, relabel data, block usage, or trigger retraining workflows
• WatchTower for AI & Data to provide continuous visibility, alerts, and built-in remediation across AI pipelines

By grounding AI governance in real data context, BigID bridges security, privacy, compliance, and governance – moving organizations beyond visibility to actionable control.

Responsible AI in Action: Key Takeaways from Cognizant and BigID

To explore how these principles translate into real-world execution, Cognizant and BigID recently partnered on the webinar Responsible AI in Action: Automating Privacy, Compliance, and Trust. The discussion highlighted several themes enterprises are grappling with as they advance agentic AI initiatives:

• Responsible AI starts with clear principles—fairness, transparency, accountability, security, and human-centric design must shape how AI systems are built and measured
• Compliance must be embedded, not bolted on, with controls integrated directly into the AI and software development lifecycle
• Automation enables both speed and trust, allowing organizations to innovate quickly while maintaining governance and privacy
• Cross-functional ownership is essential, with AI councils spanning technology, privacy, legal, risk, and business teams
• Transparency builds durable trust, supported by measurable outcomes and shared accountability

Building a Trusted AI Future

Agentic AI holds enormous promise—but only if trust is built into its foundation. Cognizant and BigID together provide a powerful combination of governance frameworks, privacy automation, and continuous data-driven oversight to help enterprises deploy AI responsibly.

As AI agents become embedded in core business functions, trust will be the defining factor for large-scale adoption. Organizations that operationalize trust—from data governance and consent enforcement to model oversight and remediation—will not only mitigate legal, ethical, and security risks, but also unlock faster innovation, stronger ROI, and more reliable AI outcomes.

In the era of agentic AI, trust is no longer aspirational. It is operational – and it is essential.

Authors

Neil Patel

Senior Director, Head of Global Product Marketing

Neil is a technology leader focused on helping organizations harness the power of AI and data to work smarter, innovate faster, and create meaningful impact. He brings new technologies to market in ways that drive clarity, accelerate adoption, and enable teams to push their missions forward.

Siddhartha Kanoria

Associate Director, Data Privacy, Cognizant

Siddhartha Kanoria is a seasoned professional in the Data Management and Data Privacy space, specializing in designing resilient data architectures that fuel AI innovation while ensuring rigorous compliance. His expertise lies in harmonizing Data Governance with emerging AI Governance frameworks, ensuring that the data fuelling autonomous systems is not only high-quality but also ethically sourced and transparently managed

Kumar Nishant

Senior Manager, Data Privacy, Cognizant

Kumar Nishant is a seasoned professional in the Data Privacy space with experience in driving complex privacy programs and initiatives across multiple industries and geographies. He specializes in data privacy, regulatory compliance, governance frameworks, and privacy technology solutions. Nishant has successfully led projects involving privacy automation tools and workflows, enabling organizations to streamline compliance and reduce risk. With a ability to translate intricate Privacy requirements into practical, scalable solutions, he helps global enterprises achieve robust privacy and data privacy standards