Thanks to AI, a full-fledged Continuous Threat Exposure Management (CTEM) program is no longer just a theory: it’s reality.
CTEM has been a security pipe dream for some time because of what it claims to do: when fully operationalized, a CTEM approach proactively finds and fixes security issues ahead of time and does it based on what’s best for the business—not flat vulnerability scores alone.
And it does it on an ongoing basis because, as SANS instructor Jonathan Risto says, “attackers aren’t waiting for a quarterly scan.” No more waiting to be surprised; no more playing catch-up with attackers.
If put into practice, AI-powered CTEM could upend the way security teams operate from the top level down. It would place resources where they are needed the most and more easily justify security decisions to board members.
Now, thanks to the infusion of AI, that kind of CTEM is going to market as more than a hypothetical possibility. It’s here now, and this blog will explain how AI made it happen.
How Did CTEM Function Until Now?
The idea of continuously managing threat exposures on a consistent basis is a lofty one, especially in the age of AI-driven threats and ever-expanding attack surfaces. And yet, it is the approach that best fits the problems we have of playing reactive security—and losing, more often than not.
However, without AI to complete the picture, the work that a CTEM program required was not feasible for many organizations.
This is because CTEM means so much more than ranking vulnerabilities. CTEM is the logical extension of exposure management, a “strategic, business-centric approach to cybersecurity” that prioritizes fixes based on more than a list of found CVEs.
Instead, it ranks them based on which have the potential to be the most impactful on the business. And to understand what has the biggest impact overall from that standpoint requires a lot of triangulation.
In a nutshell, the "pre-AI" approach would require teams to go through the following five steps manually—or armed with scripts alone:
- Scope: Find all the assets you want to protect across the attack surface. This could include shadow assets that are so named for a reason.
- Discover: Identify the exposures on each one—making sure none are missed could take time.
- Prioritize: Prioritize found exposures by business impact. This requires multi-point analysis and transcends the simple logic of “low, medium, high and critical.”
- Validate: Finding which are most easily exploited in a real-world scenario with pen testing and red teaming.
- Mobilize: Last is the most important step: assembling all the mitigation moving parts, from investigation to correlation to remediation and all the systems and processes in between, in the most efficient manner.
All these things are theoretically possible with a purely human team, which was why CTEM edged its way forward in the industry for so long. But without the force-multiplying aid, putting it into practice was another story.
Teams are already fighting to staff full SOCs and get the expertise they need for day-to-day reactive security tasks; doing all these “ahead of time” planning functions (and doing them at scale) was simply not realistic.
Until AI came along and changed the way CTEM is done.
How Does AI Enhance the 5 Key Stages of a CTEM Program?
The best way to prove the efficacy of an AI-enhanced CTEM program is to take it step by step. There are 5 key stages to CTEM, and each can be improved upon with the savvy use of artificial intelligence.
Step 1: Scope
At this point, teams want to establish the scope of their ongoing CTEM assessments. Define the assets you want to test, prioritizing the critical ones and the systems most impactful to the business.
Now you have to find those pre-defined assets, extending your reach to unpatchable attack surfaces—just like attackers do. As cyber risk advisory firm Kroll explains, “Increased use of the cloud, social media, and the digital supply chain increase attack surfaces and [create] an unpatchable layer of exposure for businesses.”
How AI Helps:
AI can discover, classify, and prioritize assets across the enterprise better and faster than humans alone. It’s called shadow IT for a reason; human SOCs overlook beta APIs, forgotten devices, and more that gets pushed to the shadow realm and unprotected.
Sometimes big things get missed. AI can help discover all the assets within a defined scope and bring them to the forefront of awareness and visibility for testing.
Step 2: Discovery
Once assets are defined and accounted for, discover the exposures on each one, using that “beyond the patch” mindset. Look for things like:
- Misconfigurations
- Insecure credentials
- Open ports
- Excessive permissions
- Unapproved services and devices
For starters.
How AI Helps:
AI can ingest environment-wide telemetry from a multitude of different sources, bringing together all found flaws in a centralized platform for quick look-up. Get a bird’s eye view of all vulnerabilities, identity exposures, vulnerabilities, and assets.
Dig deeper with GenAI Natural Language Query (NLQ) capabilities, found in the best exposure management platforms. Ask a question in plain English: “Show me all internet-facing assets with NO authentication,” and get a clear-cut spreadsheet listing.
Or ask even more granular, complex questions and get a human-readable response in a narrative-style paragraph with double-click opportunities for more details.
Step 3: Prioritization
Determine which order you are going to remediate found exposures and do it with the end-goal in mind: business viability now and in the future. Security does not exist for security’s sake, so pan out from just risk scores alone. Instead, look at a mix of:
- Exposure severity (in and of itself)
- How critical the asset is, or its data
- Potential impact should it be compromised
- How likely the asset is to be compromised
- The amount of resources needed to remediate
How AI Helps:
This is where exposure management in cybersecurity comes in. A solid exposure management platform can help you prioritize flaws based on more than flat comparisons like CVSS scores alone, and this is done through the use of AI.
AI can throw together custom dashboards and help you refine searches as you continue to ask the right NLQ questions: “Which priorities are most likely to be exploited within the next 30 days?”
This levels the playing field, so you don’t need a team of 30-year industry veterans to comprehend the risks to your enterprise at-a-glance. AI makes it feasible for nearly any admin to prioritize remediation based on severity, criticality, impact, and business goals. The result may surprise you: A medium-level CVE on a sensitive customer database may get higher priority than a critical one on a defunct server. Context matters.
Step 4: Validation
Make sure the answers you came up with are right. Run your list of priorities and exposed assets through the gamut by subjecting them to offensive techniques: pen tests and red/purple team engagements.
How AI Helps:
With the right exposure management product, teams can get straightforward attack-path analysis. Don’t just see what assets are at risk; see how they can be affected and ask your questions in real-time.
Natural Language Processing will produce answers that are easily read and understood, and answer questions like “tell me more about this step.” They can also ingest the data from the pen test and red team results, using that to refine context and help validate initial determinations.
Step 5: Mobilization
Put all this knowledge to good use. After getting AI-validated and vetted insights, it’s time to instigate mitigation.
This involves bringing other people and teams into the mix, implementing the proper security controls, stopping malicious processes, and doing it all in a workflow that makes sense.
How AI Helps:
AI can start with the basics, presenting complex mitigation steps in layman’s terms: “Here’s how to remediate this attack path.”
It can then integrate with ITSM systems to trigger the proper workflows from a centralized exposure management platform, putting the right pieces in play. Track progress across dashboards and get continuous visibility over the changes.
AI: The Logical Next Step for CTEM
CTEM is a mindset that makes sense: figure out what’s wrong with your enterprise before adversaries do—and keep figuring it out, every day. Gartner asserts: “By 2026, organizations prioritizing their security investments based on a CTEM program will realize a two-thirds reduction in breaches.”
But the only way for today’s organizations to optimize all CTEM capabilities in practice, and at scale, is to let AI do the heavy lifting. The five steps might be simple to understand, but they aren’t easy.
Many teams are lacking the resources – either in time, funding, or expertise – to keep up with these essential steps on a continuing basis. AI levels the playing field, putting Continuous Threat Exposure Management within the reach of all companies of all security levels.
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.
