Gartner: The Market for Securing AI Will Nearly Hit $5 Billion in 2027

For most of the decade, the AI conversation has been about who can build the most capable model. On August 26, 2026, Gartner published a forecast that quietly reframes the question: who is going to secure all of it?

The analyst firm now projects that the global market for securing AI — the software and services that keep AI systems themselves from being attacked, abused, or manipulated — will reach almost $4.8 billion in 2027, a 68.7% increase over 2026. By 2028, the market is forecast to hit almost $7.7 billion. For a category that barely existed as a line item three years ago, that is extraordinary growth, and it signals a structural shift in how enterprises budget for AI: capability spending and protection spending are now rising together.

What “Securing AI” Actually Means

Gartner’s forecast covers the tools that protect AI systems specifically — not general-purpose cybersecurity with an AI sticker on it. The category includes AI application security, AI usage control, AI gateways, runtime protection for models and agents, and monitoring aimed at threats that only exist because the workload is AI: prompt injection, model abuse, poisoned dependencies, and agentic systems taking actions they shouldn’t.

The near-term numbers, as laid out in the announcement:

• AI application security will remain the largest spending category in 2027, forecast to reach almost $851 million.
• AI usage control comes next at $749 million — but with the largest growth rate, 73%.
• The AI gateway segment follows at 70.9% growth, as organizations put dedicated, inspectable chokepoints between users, agents, and models.

That ordering matters. The biggest bucket today is defensive plumbing around AI applications. But the fastest-growing buckets — usage control and gateways — are the ones that deal with behavior: what the AI is allowed to do, what data it can touch, and which actions it can take on a user’s behalf. In other words, enterprises are starting to spend not just on keeping attackers out, but on keeping their own increasingly autonomous systems in check.

Why Now: Agents, Injections, and Supply Chains

Shailendra Upadhyay, senior principal analyst at Gartner, tied the surge to “the urgent need for enterprises to secure AI systems, address emerging vulnerabilities and strengthen defenses against sophisticated cyberthreats” — compounded, he noted, by growing vulnerabilities and supply chain attacks involving third-party and open-source software in AI projects. His warning is blunt: without adequate security and visibility controls, enterprise AI initiatives face a high risk of failure.

Two dynamics are driving the spending:

1. Agentic AI changes the attack surface. An AI agent that can browse, call APIs, spend tokens, and take actions is a fundamentally different security object than a chatbot. Gartner’s report projects that over half of successful cyberattacks on AI agents by 2029 will exploit access-control weaknesses and prompt injection — two failure modes that traditional security tooling simply doesn’t see.

2. The AI supply chain is software with extra steps. Foundation models, fine-tuning data, open-weight checkpoints, retrieval pipelines, and third-party plugins all enter the enterprise from outside. Each is a vector. The industry learned this the hard way in 2025–2026 as incidents involving poisoned packages, leaked tokens in agent frameworks, and compromised model hubs moved from theoretical to actual.

There’s also a feedback loop: as defenses like AI runtime protection and dedicated gateways become more reliable, organizations gain confidence in them, which accelerates adoption, which grows the market further. Upadhyay makes exactly this point — trust in the tooling is itself a growth driver.

Context: A Small Number Inside a Very Big Shift

$4.8 billion sounds modest next to the trillion-dollar headlines AI usually generates. That’s precisely what makes it interesting. Gartner’s own adjacent forecasts show the asymmetry: worldwide AI-optimized IaaS spending is projected to grow 96% in 2026 to reach $42 billion, and total AI spending forecasts run into the trillions by decade’s end. Against that backdrop, enterprises are projected to spend roughly one cent on securing AI for every dollar of AI infrastructure they stand up.

The gap is the story. Every forecast in this space — including Gartner’s companion projection that the securing-AI market continues climbing toward $16.4 billion by 2030 — is essentially a bet that the ratio of protection to capability spending must rise, because the alternative is agentic systems deployed without visibility or control.

And the pressure isn’t theoretical. Regulators are already asking pointed questions about AI safety failures — see the state-level investigations into model behavior launched this month — and boards increasingly treat a prompt-injection incident or an agent gone rogue as a disclosure-grade event. Security spending is following the risk.

The Takeaway

Gartner’s forecast is best read not as a market-sizing exercise but as a lagging indicator of a decision enterprises have already made: AI is now production infrastructure, and production infrastructure gets a security budget. The composition of that budget — usage control and gateways outgrowing everything else — tells you where practitioners expect the pain to be: not in the model weights, but in what autonomous systems do.

For security vendors, this is the land-grab phase: the categories are young, no one owns “the AI firewall” yet, and a 68.7% growth rate forgives a lot of product rough edges. For enterprises, the actionable version is simpler — if your AI rollout doesn’t have a gateway, usage controls, and runtime monitoring in the architecture diagram, you’re part of the reason this market is growing 68.7% a year.