Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Manoj Nair, Chief Innovation Officer at Snyk
As 2026 approaches, AI isn’t just speeding up software development – it’s rewriting the rules of how systems are built and secured. Conversations with customers, partners, and security leaders reveal a clear message: traditional security models weren’t designed for autonomous, agent-driven environments.
At the same time that attackers are also using agentic ai to autonomously test defenses of companies at all layers, and we are already seeing this with some major news from all the foundational model companies in late 2025
Attackers are already weaponizing agentic AI to autonomously test enterprise defenses across cloud, identity, data, and application layers. Throughout late 2025, major foundational model companies disclosed incidents where autonomous agents were able to chain actions, probe for weaknesses, and escalate capabilities without human direction. This shift marks the beginning of an AI-vs-AI security arms race-one where defensive tools operating at human speed simply cannot keep pace.
The following five predictions highlight the developments that will define AI-native security and governance in 2026.
Agency Hijacking Becomes the #1 AI Attack Vector
In 2026, the most consequential AI security threat will shift from prompt injection to agency hijacking-attacks that manipulate an agent’s tools, memory, or extended context to trigger harmful actions at machine speed. As enterprises connect agents to issue trackers, CI/CD systems, messaging platforms, and internal MCP servers, attackers will increasingly target the tools and workflows the agent can reach, not the text you feed it. A single poisoned input will be enough to redirect an agent into misusing trusted systems, creating breaches that unfold far faster than human security teams can intervene.
AI Agents Overtake Humans as the Top Insider Threat
By the end of 2026, AI agents will eclipse humans as the biggest source of insider-style data leaks-not out of malice, but because they can be manipulated, confused, or misconfigured at scale. Agents now hold long-lived memory, interact across multiple systems, and execute actions based on inferred context rather than explicit instructions. As these agents take on operational workloads, organizations will see a rise in accidental data exposure and unauthorized actions driven by poisoned instructions or subtle behavioral drift. The insider-threat model will need to evolve quickly to account for non-human identities making high-impact decisions autonomously.
Shadow Agents Explode-Creating the Biggest Blind Spot Since Shadow IT
In 2026, a surge in shadow agentic AI will create one of the largest blind spots in enterprise security. Business and engineering teams are already spinning up internal agents-via MCP, workflow tools, and lightweight automation frameworks-without formal review, tracking, or guardrails. These agents often end up with broad access to corporate data and systems, yet operate with no standard for auditing, onboarding, or policy enforcement. As their footprint expands, enterprises will discover dozens or hundreds of unsanctioned agents acting as unmonitored digital insiders, forcing companies to rethink how they inventory, govern, and decommission autonomous software.
The AI-BOM Becomes Mandatory for Enterprise Governance
By late 2026, the AI-BOM-a full inventory of every model, agent, tool, integration, and MCP connection an AI system relies on-will become a baseline requirement for enterprise governance and vendor risk assessments. As AI-native architectures grow more dynamic and interconnected, organizations will no longer accept black-box systems or undocumented agent tooling. They will demand traceability across the entire AI supply chain, including model versions, tool permissions, memory stores, data flows, and runtime actions. Much like the SBOM transformed software supply chain security, the AI-BOM will become a foundational control for understanding and securing AI at scale.
Security at Human Speed Fails-Agentic Security Becomes Required, Not Optional
By 2026, human-speed security operations will simply be unable to keep up with AI-native environments. Enterprises will shift toward agentic security models-systems that can detect, reason, and respond autonomously, using the same machine-speed workflows as the AI agents they’re protecting. Traditional rules-based monitoring won’t keep pace with non-deterministic agent behavior, toolchains that update daily, and workflows that span dozens of integrations. Security teams will move from manually supervising every action to governing the behavior of autonomous agents, enforcing policy in real time, and adopting systems explicitly designed to make decisions at the speed AI now operates.
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ABOUT THE AUTHOR
Manoj Nair is the Chief Innovation Officer at Snyk, where he leads emerging technology initiatives and helps guide the Snyk’s long-term innovation strategy in response to evolving customer needs. With deep experience building security platforms for modern software development, Manoj focuses on helping organizations safely adopt AI-native architectures while maintaining visibility, governance, and trust at scale.
