Securing AI Agents by Design

Overview

Agentic AI represents the next stage in enterprise AI, advancing beyond simple chatbots to intelligent software agents capable of perceiving, reasoning, planning multi-step goals and executing actions through external tools or APIs. Major cloud platforms like Google Agentspace, AWS Agents and Microsoft AgentFlow have introduced tailored solutions, while self-hosted frameworks such as LangChain, LangGraph and Semantic Kernel are gaining traction among enterprises. However, the autonomy of these agents introduces new risks by expanding the a_ack su_ace. IDC forecasts that over 40% of enterprise workloads will utilize autonomous agents by 2027 (up from under 5% in 2024). Businesses must prioritize security and governance to navigate this rapid evolution.

Challenges

The rise of AI agents and Large Language Models (LLMs) has unveiled a wave of cybersecurity risks. The OWASP Top 10 for LLM and Agentic AI highlights vulnerabilities such as prompt injection, excessive agency, supply-chain compromise and model stealing, which in the context of Agentic AI translate to tangible business risks, including:

Data leaks:

A rogue “exfiltration” tool-call could expose proprietary trading strategies.

Compromised reasoning loops:

A poisoned dependency could hijack an agent’s workflow.

Fraudulent operations:

Over-permissioned customer-service bots might execute unintended actions, like filing counterfeit refunds.

Our Solution

HCLTech, together with Google Cloud and Palo Alto Networks, addresses these gaps. Key capabilities include:

Model scanning and SBOM analysis:

Verifies weights, licenses and provenance at the supply-chain phase.

AI Security Posture Management (AI-SPM):

Identifies and corrects misconfigurati ons across data stores, embeddings and vector database ACLs.

AI red teaming:

Simulates evolving attacks that mirror the agent’s tool-use graph.

Runtime LLM and agent firewall:

Ensures every prompt, response and tool call complies with governance policies while mitigating risks like data loss or malicious code.

Agent security controls:

Employs signature and behavior-based analytics to thwart spoofing, poisoning attacks or uncontrolled tool chaining.