Agentic AI Security
Building safe, autonomous, and reliable AI systems in 2026.
- Understand what agentic AI is and why the security model changes when systems can reason, use tools, and act autonomously.
- Learn the real risk surface: prompt injection, memory poisoning, tool misuse, privilege escalation, and unsupervised execution.
- Get practical guardrails: least privilege, input validation, memory management, human-in-the-loop gates, continuous monitoring, and auditability.
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Download the White Paper
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WHAT’S CHANGING
AI systems are beginning to operate independently inside enterprise environments
A new class of AI systems is emerging. Agentic AI systems plan multi step workflows, retain memory across interactions, and execute actions directly against production systems such as APIs, databases, cloud infrastructure, and internal tools.
This white paper examines those failure modes in detail and outlines how security leaders should evaluate agentic systems before they scale.
KEY FINDINGS
Where agentic AI systems fail
Observed failure patterns that emerge when reasoning, memory, and execution operate together inside production environments.
Decision integrity
Autonomous agents choose actions across multiple steps. Manipulated context can redirect decisions without triggering obvious failures.
Persistent memory
Agent memory survives across sessions. Corrupted context influences future behavior long after the original interaction ends.
Tool execution
Direct access to APIs and systems turns reasoning errors into real configuration, data, and access changes.
Architecture risk
Single, hierarchical, and decentralized agents fail differently. Authority structure determines how quickly damage spreads.
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Urban Vault 1666/A, 14th Main Road, Sector 3, HSR Layout, Bengaluru, Karnataka – 560102.
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