On May 7, 2026, Cognizant announced the launch of Cognizant Secure AI Services — a new offering built specifically for enterprises that have moved past “should we deploy AI agents?” and are now asking “how do we know they are safe to run?”

It is a meaningful pivot in the conversation. For the past two years, the dominant enterprise AI question was about speed: how fast can we implement, how many use cases can we pilot, how quickly can we demonstrate ROI. Now, as real agentic systems go live in regulated industries — finance, healthcare, insurance, logistics — a harder question is surfacing: how do you prove that your AI is behaving the way you intended?

Cognizant’s answer is what the company calls “provable trust.”

What Provable Trust Actually Means

Assumed trust is what most AI deployments run on today. You test the system, it performs well in testing, you deploy it, and you assume it continues to perform the way it did before. For traditional software, that assumption holds most of the time. For AI agents, it does not.

Agentic AI systems are adaptive. They respond differently to different inputs. They operate across multiple systems simultaneously — email, databases, customer records, financial data. They take actions, not just produce outputs. And the further they operate from human oversight, the harder it becomes to know whether they are still behaving within the boundaries you intended.

Vishal Salvi, Global Head of Cognizant’s Cybersecurity Service Line, explained the challenge directly: “AI is fundamentally changing how enterprise systems behave. These systems are adaptive, context-driven and increasingly autonomous — and securing them requires continuous assurance across build and run-time environments. With Cognizant Secure AI Services, we are helping enterprises engineer trust into AI systems from day one and to sustain that trust as those systems evolve.”

The offering is built on three components:

Secure Agent Development Lifecycle (ADLC): Security embedded across the entire process of designing, building, testing, deploying, and changing AI systems — not bolted on at the end.

Cognizant Neuro Cybersecurity: A consolidated control plane that unifies AI signals and enterprise signals for real-time threat response, anomaly correlation, and audit evidence.

Responsible AI via Cognizant Trust: A continuous assurance layer that covers traceability, policy enforcement, and compliance alignment based on client-defined requirements.

Together, these span model security, data protection, identity and access management, agent behavior controls, and AI risk management.

Why This Matters Now

The timing is not accidental. Cognizant already works with 250+ global enterprises across regulated industries on digital transformation programs. They are seeing firsthand what happens when agentic AI hits production at scale — and they are building the services their clients need for the next phase.

The broader market is moving in the same direction. Earlier in 2026, the Five Eyes cybersecurity agencies issued guidance on agentic AI adoption, specifically warning about the risks of giving agents elevated permissions without adequate monitoring. In real deployments, there have already been cases where AI agents with broad access took destructive actions faster than any human could intervene.

The question is no longer whether to govern AI agents. It is what governance at scale actually looks like in practice.

What This Means for Business

For business leaders deploying AI agents across operations, Cognizant’s announcement signals something important: AI security and governance is becoming a defined service category, not an afterthought or a DIY problem.

If you are deploying agents to handle customer interactions, automate back-office workflows, or access sensitive data, you need answers to questions that most AI vendors have not addressed. What happens when an agent encounters an input it was not trained on? How do you detect when an agent is operating outside its intended boundaries? What audit trail do you have when something goes wrong?

The companies that treat governance as infrastructure — not compliance theater — will be the ones that scale AI safely and at speed. The companies that skip it are building on sand.

At Enterprise DNA, every agentic deployment we build for clients is designed with explicit guardrails, defined escalation paths, and clear boundaries on what agents can and cannot do without human oversight. That is not a feature — it is the baseline.

If you want to understand what responsible AI deployment actually looks like for your business, talk to our advisory team.