How Agentic AI is Driving a Security-First Data Engineering Revolution

What is Agentic AI?

Agentic AI refers to autonomous AI agents with specialised skills, memory and decision-making capabilities which can mimic human behaviour in performing tasks while interfacing with enterprise applications and data sources. These agents can interact with one another, forming a complex network of interconnected agents to solve business workflows and accomplish complex goals. Agentic AI is powering the next generation of intelligent enterprise automation.

Enterprises are using Agentic AI in a variety of use cases, primarily to automate tasks, enhance customer experience, and improve decision-making.  Examples of AI agents include customer service agents, lead generation agents, financial analyst agents, and note-taking assistants, to name a few. In fact, 2025 is being called the “Year of AI Agents” by Nvidia CEO Jensen Huang.

Security Challenges in the Adoption of Agentic AI

With the growing adoption of autonomous AI systems to access and manipulate sensitive enterprise data, significant security challenges have emerged. AI Agents whether operating as single- or multi-agent applications, engage in diverse communication scenarios:

·         Agent -> Agent,

·         Agent-> Human

·         Agent -> application.

These agents are powered by Large Language Models (LLMs) or Small Language Models (SLMs) and bring known risks such as hallucination, inappropriate content, bias etc. all of which must be proactively managed.

One emerging risk arises from their autonomous behaviour and human-role mimicry.

 This is concerning the scope and extent of data which an agent can access. Without proper constraints:

• Agents might access more sensitive data than their role permits.

• In agent-to-agent communication, an agent could share data without validating the requester’s authorisation.

• The same risk exists in agent-to-human interactions, where agents may disclose sensitive information without confirming user permissions.

Another major concern is the form of prompt injections referred to popularly as ‘jailbreaking’ i.e. a user crafts a prompt to override instructions remove safeguards and return either inappropriate or unauthorised content. This risk is magnified as more agents are developed and exposed to broader user bases.

Security First Engineering

In response to escalating cybersecurity threats, global regulatory bodies and countries have introduced regulations around access and handling of data. A "Security by Design" approach is now standard for AI and data engineering projects. Security must now be embedded by design across the entire lifecycle — from agent development to deployment and inter-agent communication. Key focus areas include:

Security in Agent Interactions

o   Enforcing Role Based Access Control (RBAC) for agent-to-agent and agent-to-human interaction.

o   Encrypted and permissioned communication between agents

o   Verification of human identity and access before sharing of data

Content-Based Security

o Input/output / topical guard rails blocking sensitive input data, cleanse the output data.

o   Bias Detection and Abuse Monitoring

o   Responses and grounded in context and explainable, reducing hallucination

• Logging Traceability and explainability of all interactions.

• Security Testing, Red Teaming i.e. testing AI systems for vulnerabilities, is built into the project life cycle.

• Compliance with regulations and guidelines around responsible AI and usage of data

Strengthening India's Data Sovereignty Through Policy Compliance

India has introduced the DPDP (Digital Personal Data Protection Act) to protect individuals' digital personal data. DPDP mandates enforce strict data localization, privacy controls, and audit trails regulating the collection, storage, processing, and cross-border transfer of personal and non-personal data.

These regulations help reduce concerns about data misuse and strengthen trust in AI systems.

 India's Ministry of Electronics and Information Technology (MeitY) outlines two core regulatory principles: 

·      Traceability: of data, models, systems, and actors throughout the lifecycle of AI systems 

·      Transparency: Clearly defined contracts and accountability frameworks among stakeholders regarding risks and liabilities. 

By enforcing DPDP mandates into the operational core of agentic AI systems, organizations can uphold India’s data sovereignty while accelerating safe digital innovation.

Enhancing Competitive Differentiation 

Security First Design is essential, not optional for Agentic AI applications, especially for firms operating in fintech, healthcare, and government sectors where data integrity and compliance are non-negotiable. 

85% of CEOs say cybersecurity is critical for business growth, according to a survey by Gartner.

A Deloitte survey found that 74% of respondents from financial services institutions believed that their investment in Cybersecurity and Data would increase alongside their AI initiatives.

A PWC survey has revealed that organizations leveraging advanced compliance technologies reported 60% fewer regulatory breaches and 30-40% lower compliance costs.

Organizations that adopt secure-by-design agentic systems not only protect themselves from emerging risks but also build trust and regulatory alignment – thereby gaining a sustainable edge.

Written By -- Kanakalata Narayanan, Vice President, Engineering, Ascendion

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