During my career in cybersecurity and compliance, I have seen things change faster and faster. As the complexity of data infrastructure increases exponentially and threats increase in volume and sophistication, organizations strive to maintain high levels of security and compliance.
With so many regulatory framework requirements and increasing risks, it is difficult for cyber governance, risk and compliance (GRC) teams to maintain security, implement controls, monitor changes and manage governance and compliance. compliance. Today’s digital-first business environments are large and complex, offering a large attack surface and low visibility. For many businesses, it can take weeks just to map out all the processes, data sharing, and exit points in your ecosystem, let alone monitor and analyze them.
In my experience, there is no way for manual, siled workflows to keep up with the pace of cyber GRC operations today. When working manually, it is extremely difficult to be sure that you have gathered all the relevant data. This makes it even more difficult to feel aware of all emerging risks and able to detect new vulnerabilities. This is why artificial intelligence (AI) technologies, including Generative AI (GenAI), machine learning (ML), and natural language processing (NLP) must be part of today’s cyber GRC toolkit.
AI is already makes a difference in the IT sector at large. AI network monitoring, threat detection, penetration testing, automated remediation, and predictive analytics are just some of the use cases of AI in cybersecurity. Teams are starting to use generative AI for protocol development. AI can automate tasks, digest large volumes of data, and operate around the clock without getting bored or distracted. So it’s easy to see applications for it in cyber GRC as well.
In my opinion, compliance and security teams should consider applying AI to cyber-GRC processes this year in three primary ways: supporting continuous, real-time monitoring across executives and business units; to expedite and enable easy avenues for remediation; and to extract actionable insights from GRC documentation.
Powering continuous monitoring
GRC is a never-ending task. GRC professionals must continually keep an eye on the threat horizon for emerging risks and monitor conditions within their networks to identify gaps and quickly close them. Manual monitoring is unsuitable for this task. Humans cannot stay 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, transforming real-time triggers into GRC insights. Generative AI can analyze private data as well as public information, bringing it all together to automate early detection of cyber risks and emerging compliance gaps.
Early detection of vulnerabilities, breaches, and issues 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 to prepare audits and GRC requests from partners and customers.
Serve as a remediation assistant
There is 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 analyze the root causes, develop remediation options and decide which one would be best to follow in this situation.
GRC teams may need to think about an audit trail at the same time, which adds complexity.
AI can support and accelerate this process. AI monitoring systems collect a lot of data about network health, and AI analytics can quickly sift through it to find vital clues that guide faster root cause analysis. GRC professionals can turn to conversational interfaces powered by NLP engines to request advice on evidence collection – and even to execute changes that close compliance gaps.
We’re also seeing greater adoption of semi-automated remediation that uses human intervention to reduce resolution times. GRC teams are able to ask for suggestions on corrective actions to take and sometimes automate parts of the workflow to speed up the timeline.
Deliver actionable analytics
Until recently, GRC policies and procedures were considered static and almost irrelevant. They were formulated only once and then largely neglected. Today, cyber GRC is a dynamic and ongoing discipline.
This change has likely opened a gap between the heavy documentation your organization spent months to formulate and the ever-changing regulations and governance requirements.
Advanced AI-based technologies can fill this gap. GRC teams can apply AI to analyze large sets of documentation and extract relevant text and requirements.
AI analytics can also convert this information into dynamic data-driven governance tools that help guide GRC staff toward better processes and workflows.
Effective Cyber GRC requires AI
After spending years supporting businesses with their cybersecurity and GRC efforts, the current pace of change has convinced me that AI is the only way to keep pace. With complex networks, rapidly changing regulations, and increasing demands for proof of compliance, manual workflows cannot be enough. AI monitoring, remediation, and analysis give GRC teams the automation tools they need to meet expectations and requirements.
Arik Solomon is the co-founder and CEO of Cypago.
