2026 AI Threat Landscape Report

AI Threat Landscape 2026

How AI is transforming the threat environment — from intelligent assistant to autonomous actor. Research from 500+ security professionals.

CONTENT

THE RISE OF AGENTIC AI

AI is moving from assistant to actor. This year’s survey shows that organizations now depend on AI for revenue, customer experience, and core operations, but many security programs are still built for static models and traditional software controls. Encryption, governance, and secure deployment are becoming common, yet runtime visibility, adversarial testing, and AI-specific incident response remain uneven. As AI systems gain autonomy, connect to tools, and make decisions across workflows, the gap between AI adoption and AI security is becoming a direct business risk.

KEY FINDINGS AT A GLANCE

88% of organizations say most or all internally operated AI models are critical to business success.
78% say embedded third-party AI models are also business-critical.
69% can definitively say whether they experienced an AI security breach in the past 12 months. 31% report uncertainty.
76% say shadow AI is a definite or probable problem.
93% use open-weight models from public repositories, yet fewer than half consistently scan inbound models.

What’s New in AI

Four shifts matter most in this year’s AI threat landscape:

Models became better at deep reasoning.
Smaller edge models got stronger and cheaper to deploy.
Agentic systems moved into everyday business tools.
Protocols such as MCP, A2A, and AP2 started to standardize how agents connect to tools, other agents, and payments.

AI is now business-critical. Security has not caught up

The biggest takeaway from this year’s report is the widening gap between how AI is being deployed and how it is being secured. Many organizations have foundational controls in place, but agentic AI demands more than secure deployment and policy statements. It requires continuous accountability, runtime visibility, clear ownership, and security controls built for systems that can act on their own.

Where attackers are getting in

Public model repositories and open-weight model ecosystems
Internal and external enterprise AI chatbot systems
Agent-based and tool-using autonomous AI systems
Connected protocols such as MCP, A2A, and AP2 interfaces
Third-party AI applications and external integrations

Five threat areas every security team should watch

Data poisoning and backdoors - Very small amounts of poisoned data can compromise model behavior.
AI supply chain attacks - Models, configs, tokenizers, plugins, and third-party integrations all expand the attack surface.
Prompt injection and guardrail bypass - Guardrails are routinely bypassed or attacked directly.
Memory and RAG poisoning - Agents can be manipulated through the information they retrieve, store, or summarize.
Model evasion - Adversarial inputs continue to break vision and multimodal systems.

AI misuse is already causing real-world harm

This report documents deepfakes, political disinformation, harmful chatbot advice, AI-enabled fraud, automated cybercrime, AI-powered malware, and incidents affecting hundreds of organizations. AI risk spans cybersecurity, fraud, trust, safety, and compliance.

Discover What AI Security Leaders Recommend

Treat AI security as a business and regulatory control, not a feature add-on.
Move beyond guardrails to runtime monitoring, adversarial testing, and AI-specific incident response.
Assume AI systems are exploitable and design for containment, visibility, and fast response.
Reassess third-party AI risk, especially for models, agents, integrations, and SaaS tools.
Align AI governance with business impact.

Frequently Asked Questions

What is agentic AI?
Why is agentic AI harder to secure than traditional AI?
Are guardrails enough to secure AI?
Why does AI supply chain security matter?
What should security teams ask AI vendors?