Risk Profiler launches AI-powered threat investigation capability

In August 2026, Risk Profiler launched its KnyX Autonomous Investigations, which is an AI-powered capability designed to help organizations automate the investigation, validation, and response to external cyber threats.

The new feature, which is being showcased at Black Hat USA and DEF CON 34, is designed to automate workflows covering threat investigation, verdict generation, and policy-based remediation while maintaining governance through customer-defined controls. According to the company, KnyX uses AI agents to investigate threats such as leaked credentials, phishing websites, look-alike and typo squatted domains, vendor breach claims, and external threat intelligence feeds. The platform generates evidence-backed verdicts and can automatically execute remediation actions where permitted by organizational policies.

Risk Profiler also said each investigation follows structured, deterministic workflows and maintains a complete audit trail, including evidence, execution history, and agent versions, to support governance and compliance requirements. At launch, the platform includes autonomous agents for leaked credential investigations, password resets, phishing-page analysis, domain investigations, vendor breach validation, and threat intelligence prioritization.

Impact on the AI Market

Artificial Intelligence (AI) and Machine Learning (ML) have become a foundational aspect in modern threat detection, as it helps in enabling security teams to identify, analyze, and respond to cyber threats at a speed and scale impossible for humans alone. By automating data analysis, identifying hidden patterns, and predicting emerging risks, AI strengthens modern cybersecurity infrastructure, allowing human analysts to focus on the most critical strategic challenges.

Traditional security solutions, such as early antivirus software and intrusion detection systems, rely on signature-based detection. These systems maintain a database of digital "signatures," or unique patterns, for known malware and cyber-attacks. When a file or a network packet matches a signature in the database, the system flags it as a threat. The main limitation of this approach is that it can be reactive. It is only able to detect threats that have already been identified and added to the signature database. Thus, it proves to be utterly ineffective against new, previously unseen threats, often referred to as zero-day attacks.

This is where Artificial Intelligence comes into play. AI handles the heavy lifting in threat detection by sifting through millions of events, finding patterns, and automating responses while humans provide oversight, context, and ethical judgment. It’s not man versus machine; it’s man plus machine, working in sync to outpace cyber adversaries. In cybersecurity, ML models are trained on vast datasets of network traffic, file behaviors, and security logs to recognize normal versus malicious activities. AI, in a more general sense, can encompass everything from these ML models to more advanced, automated reasoning and decision-making systems that can take actions based on those predictions.

Impact on the Monitoring Tools Market

The global monitoring tools market size was estimated at USD 36.66 billion in 2024 and is predicted to increase from USD 43.28 billion in 2025 to approximately USD 185.78 billion by 2034, expanding at a CAGR of 17.62% from 2025 to 2034.

According to Precedence Research, the evolving IT landscape, characterized by hybrid infrastructures, cloud adoption, and diverse applications, has amplified the complexity of monitoring requirements. This complexity drives the demand for advanced monitoring tools capable of providing holistic visibility across diverse environments. Organizations worldwide are undergoing digital transformation initiatives to enhance agility, efficiency, and competitiveness. As businesses digitize their operations and services, the need for monitoring tools to ensure the performance, availability, and security of digital assets becomes indispensable, driving market growth.

The proliferation of Internet of Things (IoT) devices across various sectors, including manufacturing, healthcare, and smart cities, generates vast amounts of data. Monitoring tools equipped to handle the unique challenges of IoT environments, such as device management, data analytics, and security monitoring, witness increased demand. Businesses are increasingly focused on optimizing the performance and efficiency of their IT infrastructure and applications. Monitoring tools provide insights into system performance, resource utilization, and application behavior, enabling organizations to identify bottlenecks, optimize resources, and enhance user experiences.

Impact on the Industrial Cybersecurity Market

The global industrial cybersecurity market size was estimated at USD 26.70 billion in 2025 and is predicted to increase from USD 29.08 billion in 2026 to approximately USD 61.18 billion by 2035, expanding at a CAGR of 8.65% from 2026 to 2035.

According to Precedence Research, industrial cybersecurity has become increasingly critical due to the growing dependence on cyber technology and the escalating risks of cyberattacks. Such attacks on industrial systems can result in severe consequences, including the disruption of critical infrastructure, loss of sensitive data, and compromise of equipment and systems.

AI is profoundly impacting the industrial cybersecurity industry by enabling a shift from reactive to proactive defense mechanisms. AI-powered systems use machine learning and behavioral analytics to analyze a vast amount of data from industrial control systems and the Industrial Internet of Things (IIoT), allowing them to establish baselines of normal operations and detect subtle anomalies indicative of zero-day attacks or insider threats in real-time, which often bypass traditional signature-based methods. Furthermore, AI accelerates incident response by providing human analysts with actionable intelligence and automating routine tasks like vulnerability scanning and alert triage, thereby reducing human error and improving operational efficiency.

Expert Opinion

“Security teams do not lack detection tools. What they are increasingly drowning in is triage, with the same manual investigations being repeated thousands of times every week,” said Setu Parimi, Co-Founder and Chief Technology Officer of RiskProfiler.