Straiker Listed as a Sample Vendor in Gartner® Emerging Tech Impact Radar for AI Security Testing and AI Security Platforms

Gartner published the Emerging Tech Impact Radar: AI Cybersecurity Ecosystem. Straiker is listed as a Sample Vendor in two categories: AI Security Testing and AI Security Platforms.

I’m proud to see Straiker included in both, particularly because these two areas reflect how we believe enterprise AI security is evolving: continuously attack AI systems to understand where they can break, then use what you learn to protect them in production.

The timing also matters.

AI is already changing cybersecurity budgets. Based on research from IANS and Artico Search, roughly seven in 10 CISOs surveyed said AI was their top priority for new cybersecurity spending.

At the same time, enterprises are moving beyond chatbots toward coding agents, coworking agents, customer-facing agents, and custom agents that can access tools, data, applications, and other agents.

AI security testing has to test what agents can actually do

Traditional AI red teaming often starts and ends with prompts: Can you jailbreak the model? Can you cause it to disclose information? Can you get around a guardrail? Those remain important tests. But an AI agent has an execution environment.

Agents may be connected to MCP servers, tools, databases, infrastructure, APIs, code repositories, or other agents. Testing an agent therefore means understanding not only how the model responds, but what the agent can reach, what authority it has, and what an attacker could cause it to do. Check out Straiker's STAR Labs framework.

That is the approach behind Straiker Ascend AI.

Ascend AI begins with the agent’s application context and then performs reconnaissance against the environment to fingerprint the real attack surface including MCP servers, tools, database access, and infrastructure. A separate Attack Agent then attempts to exploit that surface using a mix of fine-tuned attack models, frontier models, adaptive prompting, and attack strategies that change as the target responds.

It is closer to attacking a live system than running a static checklist.

Gartner describes AI Security Testing as encompassing offensive techniques including automated adversarial prompts and, for agents, expanding testing into both the cognitive and execution loops.That distinction will only become more important as agents gain more autonomy.

AI security is becoming a platform problem

The second category where Straiker is listed — AI Security Platforms — maps closely to the larger vision we have been building toward.

We call it attack to defend.

Discover AI gives organizations visibility into their agentic estate: agents, MCP servers, Agent Skills, tools, models, integrations, permissions, and connections.

Ascend AI actively attacks those systems to uncover exploitable paths and unsafe behavior.

Defend AI uses runtime controls to detect and stop malicious or unauthorized behavior — including the ability to contain an agent when necessary.

But the real value is the loop between them.

What you discover should determine what you test. What you find during testing should improve runtime controls. What happens at runtime should create better adversarial tests.

Discover. Attack. Defend. Repeat.

That closed-loop approach is how we believe enterprises move from periodically assessing AI risk to continuously securing AI as it changes.

AI security is becoming a real budget line

This is perhaps the most interesting implication of the Gartner research for security leaders.

Both AI Security Testing and AI Security Platforms sit in the three-to-six-year range of the Impact Radar. Gartner explains that this range represents the expected time until an emerging technology crosses from early-adopter to early-majority adoption.

For organizations planning their 2027 security investments, that is an important distinction.

AI security is no longer just a question of whether employees are using ChatGPT or Claude or Gemini. Enterprises now need to think about AI agent discovery, agent security posture, adversarial AI testing, runtime protection, MCP and Agent Skills security, and agent containment as part of the security architecture surrounding enterprise AI.

Being listed by Gartner as a Sample Vendor in both AI Security Testing and AI Security Platforms is something our team is proud of.

More importantly, it reinforces where we are focused: building the security layer that allows enterprises to deploy increasingly capable AI while maintaining visibility, control, and the ability to act when an agent crosses the line.

‍

Source: Gartner, Emerging Tech Impact Radar: AI Cybersecurity Ecosystem, David Senf, Mark Wah, Tarun Rohilla, Marissa Schmidt, Evan Zeng, Alfredo Ramirez IV, Tuong Nguyen, Walker Black, 5 October 2026.

Gartner Objectivity Disclaimer: Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.