My main use case for Straiker is securing AI-powered applications and autonomous agents, which helps identify potential security risks, monitor agent activities, detect prompt injection attempts, and prevent unauthorized access to sensitive data and connected tools.

A practical example of how I have used Straiker to secure an AI-powered application is when it is connected to internal APIs and databases, helping identify risky agent permissions, test whether prompt injection could manipulate the agent, and detect unauthorized tool usage before it leads to data exposure.

Straiker has positively impacted my organization by improving AI security visibility through the identification of unknown agents, risky integrations, and excessive permissions.

In terms of outcomes, the biggest expected benefit from improved visibility is having better insight into AI-related security risks and reducing manual effort in identifying vulnerabilities, which helps my teams detect unsafe agent behavior earlier, although I do not have verified financial or percentage-based metrics to share.

With tools like Claude Code and ChatGPT Enterprise, Straiker helps provide a centralized view of AI usage, agent activity, connected tools, and potential security risks, which is particularly valuable when employees use different AI platforms and agentic applications across the business, helping identify risky integrations, excessive permissions, potential data exposure, and suspicious agent behavior, making it much more difficult to manage risks consistently without centralized visibility.

Before deploying it, Straiker highlighted how many AI integrations and agent interactions can exist beyond what security teams initially track, while also bringing attention to potential risks such as excessive tool permissions, unmonitored agent activities, prompt injection, and possible sensitive data exposure, with the biggest takeaway being the importance of having centralized visibility and consistent security policies across the entire AI environment.

Straiker has changed the way my team evaluates whether an AI application or agent is safe to put into production by encouraging a more security-focused approach. Instead of relying only on functional testing, we now also evaluate prompt injection risks, agent permissions, data exposure, and tool interactions, while adversarial testing helps identify weaknesses early, and runtime protection adds another layer of security, thereby making production readiness more structured and risk-driven.

The best features that I find most valuable in Straiker include AI agent discovery and inventory, prompt injection detection, automated AI red teaming, MCP server and tool security, runtime threat detection and prevention, data exfiltration protection, AI agent governance, and visibility.

Among these features, the one that has had the biggest impact on my work is the ability to improve AI security visibility by identifying unknown agents, risky integrations, and excessive permissions, helping security teams understand potential attack paths and strengthen protection before deploying AI applications into production.

Governance and security are essential when AI agents can access business data and execute actions independently, and I appreciate capabilities such as agent discovery, permission visibility, policy enforcement, and audit trails, ensuring human oversight and least privilege access remain important parts of any AI security strategy.

Regarding Straiker's AI capabilities, security findings can help identify vulnerabilities and suspicious agent behavior; however, I believe findings should be validated against actual application behavior before taking action, where accuracy, low false positive rates, and clear explanations of detected risks are important for building confidence.

The difference between generic LLM guardrails and Straiker's ability lies in that generic LLM guardrails mainly focus on filtering prompts and responses, whereas Straiker provides a broader security approach by monitoring agent behavior, tool interactions, and potential threats at runtime, giving better visibility into risks like prompt injection, unauthorized tool usage, and sensitive data exposure, with the key advantage being context-aware detection rather than relying only on static rules, although detection accuracy, false positive rates, and latency should be validated under real-world workloads before making a firm comparison.

In terms of value, having discovery, adversarial testing, runtime defense, and the ability to shut down or contain an agent in one platform is very valuable because it provides a unified approach to AI security, helps identify AI agents and their integrations through discovery, exposes vulnerabilities before deployment with adversarial testing, and detects and contains threats during execution with runtime defense, thus reducing tool complexity, improving visibility, and making incident response more efficient.

It is extremely important for my security team to have one way to understand and govern AI use across coding agents, enterprise assistants, chatbots, and custom agentic applications, as managing them separately creates security gaps and increases operational complexity. A unified platform provides consistent security policy, centralized visibility, access control, and auditability across all AI environments, thereby making it easier for security teams to identify risk and enforce governance standards as AI adoption grows.

When Straiker identifies a risk, the information is actionable when it clearly explains the detected risk, its potential impact, and the affected AI agent or integration, helping security and engineering teams prioritize issues, understand the attack path, and take corrective action, with actionable recommendations being particularly valued over generic alerts for making remediation faster and more effective.

Straiker fits well into a diverse AI environment because its approach is designed to support different models, AI applications, agent frameworks, and cloud environments, while I appreciate the ability to apply consistent security controls without depending on a single model provider, with integration into existing gateways and workflows being important for improving security visibility without requiring major changes to the existing architecture.

I would like to see more integration with cloud platforms, SIEM tools, and enterprise security solutions, as well as more customizable dashboards, detailed reporting, simpler policy configuration, and clear risk prioritization to make Straiker even more useful.

I have been using Straiker for the last six months.

Straiker is stable.

The vendor-agnostic approach is useful for organizations running AI agents across multiple frameworks and cloud platforms, designed to support different agent architectures and growing AI environments, while actual scalability should be validated against agent volume, telemetry requirements, and deployment architecture.

Customer support is good, although I did not get a chance to connect with them.

Previously, AI security was handled through application-level security controls, API security, access management, and manual testing, and I switched to Straiker because it provides a more specialized approach focused on AI agents, prompt injection, MCP integration, and runtime threats.

The main potential ROI comes from reducing manual AI security assessments, identifying vulnerabilities earlier, and preventing data exposure or unauthorized actions, helping security teams manage growing AI environments more efficiently, although I do not have verified financial figures to quantify the savings.

Pricing depends on the deployment model, number of AI agents, integration, and security requirements, and I will evaluate the licensing structure, implementation effort, and ongoing operational costs before making a purchase decision.

Before choosing Straiker, I evaluated relevant alternatives such as Microsoft AI security capabilities, Lakera, Protect AI, and other AI security platforms while comparing agent discovery, adversarial testing, runtime protection, integration, support, and pricing.

I rate Straiker eight out of ten.

I chose eight out of ten because Straiker offers a strong approach to AI agent security through discovery, adversarial testing, and runtime protection, with its ability to secure agent interactions and connected tools being particularly valuable as organizations adopt more AI-driven workflows.

Straiker supports SaaS and on-premise deployment options for its runtime protection capabilities, and depending on the product and environment, it can be integrated with different AI frameworks, cloud platforms, APIs, and agent workflows, while I am currently using it as a SaaS product.

I use AWS as the cloud provider for Straiker's SaaS deployment.

I recommend that others first identify all the AI agents, APIs, MCP servers, and connected tools in their environment, run security tests against real AI workflows, and evaluate prompt injection detection, runtime protection, and policy enforcement, while starting with a proof of concept before expanding across the organization.