Simulating Your Worst-Case Scenario and Keeping Tabs on New Threats Using AI Systems

Most companies still do not have full visibility over possible attack paths, leaving them blind to the emerging new ways attackers can enter their network. And as more employees are using AI in the workplace, attack paths are getting trickier. Even if AI components don’t account for the full attack story, they increasingly account for a part.

• 43% admit to putting business-sensitive data into AI models, according to the most recent Oh, Behave! report.
• 68% of employees use personal AI accounts for work.
• A Samsung (News - Alert) employee recently uploaded a sensitive chunk of code to a public AI tool; in response, the company banned AI usage outright.

These are just a few examples of how AI is pervading everyday work life, and not for the safer. As we consider “worst-case-scenarios" in the workplace, AI increasingly needs to feature in these discussions.

And to catch AI, you need AI.

In this blog, we’ll review how AI systems are being used to give companies a comprehensive, continuous view into the attack paths adversaries will take; attack paths that are now more likely than not to involve AI.

1.AI Systems for Exposure Management

Every fatal attack path starts with an initial exposure.  AI-powered systems are used to uncover these exposures and help teams stay one step ahead. Specifically:

• Exposure management in cybersecurity is a business-centric approach that uses AI to discover assets and their vulnerabilities across complex landscapes, then prioritize fixes based on business impact. 
• CTEM takes exposure management to the next level by notifying you of exposures on an ongoing basis.

GenAI-powered exposure management propels this process even further, covering the cyber skills gap and making complex exposures comprehensible to even junior analysts through Natural Language Queries (NLQs), as explained below.

2.AI Systems for Attack Path Analysis

The best exposure assessment platforms draw exposures out to their logical end by using AI to simulate possible kill chains based on those exposures. That way, teams are prepared for anything. They:

• Visualize the relationships between assets, identities, and vulnerabilities
• Model exploit chains for security teams to see
• Prioritize paths based on risk to the business

Here’s how that works up-close.

• Data aggregation: The AI-driven CTEM platform ingests telemetry (vulnerabilities, identities, permissions, asset inventory, cloud configurations) from across your tools.
• Graph construction: The platform builds a model showcasing the attack path based on the aggregated data.
  • This looks like: “Internet-based attacker --> exposed VM --> vulnerability X --> elevate to local admin --> move laterally to CRM database --> compromise domain controller --> exfiltrate data.”
  • Plus, using generative AI, users can query the platform in plain English (“What is the impact to the business if this path is exploited?”) and get an answer in the same way.
• AI reasoning and readability: Using AI, the CTEM solution presents the findings in a simple, narrative style: “This is how an attacker could reach your CRM database in seven steps...”
• Business logic adaptation: This is where the priority piece fits in. AI reasoning puts the pieces together (between identity, asset criticality, permissions) and assigns severity based on what’s most important to your business.
  • So, a high-impact asset like an HR payroll server or CRM database would get highlighted more than an attack path leading to a defunct API.
• Step-by-step suggestions: CTEM solutions don’t just drop the problem at your door and leave you there. Using the tools they know you have,they offer step-by-step remediation procedures for eradicating the threat.
  • For example: “Remove admin access from Software Engineer group 3,” “apply patch 1.8.4 to the system VM,” and so forth.

This is essentially a post-red teaming rundown with all the blue team benefits, but in real-time and available round-the-clock.

3.AI Systems for Business-Centric Threat Prioritization

Once you understand your exposures and where they can lead, which attack paths do you pursue first?

CTEM solutions use AI to pull context, analyze asset severity and exploitation likelihood, and suggest the best course of action based on total business impact. No more flat CVSS scores; no more wasted cycles doing non-mission-critical remediations while more important fixes fall by the wayside.

This means that on a day-to-day basis, your SOC would no longer be wasting time clearing out the backlog arbitrarily from top to bottom. Instead, it would be placing its resources where it matters most: closing actual doors along the kill chain that have been verified to lead directly from current exposures to business-critical assets. It would be working on those exposures first.

4.AI Systems to Enforce AI Acceptable Use Policies

Uncovering attack paths may surface some uncomfortable truths, like how often employees use unsanctioned AI tools for their daily tasks, and how much information is being run through them.

This knowledge prompts the creation of AI acceptable use policies (AUPs). These are guidelines for the appropriate ways in which employees are permitted to use AI systems for work. They include:

• Lists of authorized AI tools
• Rules on data classification
• Usage and accountability guidelines

Making these AI acceptable use policies is the first step; enforcing them is the critical one. New, AI-aware tools are coming to market that can help organizations discover all instances of AI tool usage within their environments. These AI security systems empower teams to:

• Discover when GenAI is being used throughout the organization, and what type
• Uncover data exposed to AI models
• Enforce AI AUPs across the board

Keeping Ahead of Changing Threats

As organizations expand, they are constantly exposing themselves to “worst-case scenarios,” especially where AI is concerned. These evolve faster than point-in-time solutions can keep up.

As the SANS Institute states,

“Rather than waiting for the results of a quarterly scan or an annual penetration test to determine what needs to be addressed, continuous monitoring proactively and repeatedly assesses and reassesses the current security posture for potential weaknesses.” Regarding AI, these weaknesses include potential shadow use, potential misuse, and potential sensitive data exposures.

As companies harness the potential CTEM and AI-aware security tools, they can continuously keep ahead of modern, AI-informed security threats—at any time, with any skill level, and anywhere along the attack path.

About the author:
An ardent believer in personal data privacy and the technology behind it, Katrina Thompson is a freelance writer leaning into encryption, data privacy legislation, and the intersection of information technology and human rights. She has written for Bora, Venafi, Tripwire (News - Alert), and many other sites.