Over the course of my career in the cyber and compliance spaces, I've seen things evolving faster and faster. As data infrastructure complexity grows exponentially, and as threats grow in volume and sophistication, organizations are racing to maintain high levels of security and compliance.
With so many regulatory framework requirements and growing risks, it's tough for cyber governance, risk, and compliance (GRC) teams to maintain security, implement controls, monitor changes, and manage governance and compliance. Today's digital-first business environments are sprawling and complex, offering a large attack surface and poor visibility. For many companies, it can take weeks just to map all the processes, data sharing, and exit points in your ecosystem, let alone monitor and analyze them.
Based on my experience, there's no way for manual and siloed workflows to cope with the pace of cyber GRC operations today. Working manually, it's immensely difficult to be confident that you've gathered all the relevant data, so it's even more of a stretch to feel that you're on top of all emerging risks and able to spot new vulnerabilities. That's why artificial intelligence (AI) technologies, including generative AI (GenAI), machine learning (ML), and natural language processing (NLP), have to be part of today's cyber GRC toolkit.
AI is already making a difference in the broader IT industry. AI network monitoring, threat detection, pen testing, automated remediation, and predictive analytics are just some of the use cases for AI in cybersecurity. Teams are beginning to put generative AI to work at developing protocols. AI can automate tasks, assimilate high volumes of data, and operate around the clock without getting bored or distracted, so it's easy to see applications for it throughout cyber GRC too.
In my opinion, there are three main ways that compliance and security teams should consider applying AI to cyber GRC processes this year: to support continuous, real-time monitoring across frameworks and business units; to speed up and enable easy paths for remediation; and to extract actionable insights from GRC-related documentation.
Powering Continuous Monitoring
GRC is a never-ending task. GRC professionals need to continually keep their eyes on the threat horizon for emerging risks and monitor conditions within their networks to identify gaps and swiftly close them. Manual surveillance is inadequate for this task. Humans can't remain focused 24/7, so compliance gaps can go unnoticed and even serious risk triggers can be overlooked.
Generative AI tools and machine learning technologies are better suited than human monitoring. You can run these tools in the background where they track every interaction and access request, turning real-time triggers into GRC insights. Generative AI can analyze private data as well as public information, bringing it all together to automate the early detection of cyber risks and nascent cracks in compliance.
Timely detection of vulnerabilities, breaches, and glitches allows GRC teams to act sooner, neutralize GRC issues before they become serious, and formulate more effective mitigation strategies. The relevant data collected by real-time monitoring is also an asset when preparing for audits and GRC requests from partners and customers.
Serving as a Remediation Assistant
There's no time to waste between gap detection, risk analysis, and remediation. Mean time to resolution (MTTR) is a vital metric for network health. But it takes time to run root cause analysis, work out remediation options, and decide which one would be best to follow in this situation.
GRC teams might need to think about an audit trail at the same time, adding to the complexity.
AI can support and speed up this process. AI monitoring systems gather extensive data about network conditions, and AI analytics can quickly sift through it for vital clues that guide faster root cause analysis. GRC professionals can turn to conversational interfaces powered by NLP engines to ask for guidance about evidence collection — and even to execute changes that close compliance gaps.
We're also seeing more adoption of semi-automated remediation that uses human-in-the-loop to shorten resolution lag times. GRC teams are able to request suggestions for steps to take for remediation, and sometimes automate parts of the workflow to speed up the timeline.
Delivering Actionable Analytics
Up until recently, GRC policies and procedures were considered to be static and almost irrelevant. They were formulated once and then largely left overlooked. Today, cyber GRC is a dynamic, ongoing discipline.
This change has likely opened up a gap between the ponderous documentation that your organization took months to formulate and constantly moving governance regulations and conditions.
Advanced AI-based technologies can bridge that gap. GRC teams can apply AI to scan large sets of documentation, and extract relevant text and requirements.
AI analytics can also convert this information into dynamic, data-based governance tools that help guide GRC personnel toward better processes and workflows.
Effective Cyber GRC Requires AI
Having spent years supporting companies with their cybersecurity and GRC efforts, the current pace of change has convinced me that AI is the only way to keep up. With complex networks, rapidly shifting regulations, and increasing demands for proof of compliance, manual workflows can't cut it. AI monitoring, remediation, and analytics give GRC teams the automation tools they need to meet expectations and requirements.
Arik Solomon is the co-founder and CEO of Cypago.