Can Your Exposure Management Strategy Handle AI-Powered Cyber Attacks?

The cybersecurity landscape has always been a relentless arms race. But the advent of AI (artificial intelligence) is changing its face even more dramatically.

Virtually any threat actor can now automate, scale, and refine with AI what was once the exclusive domain of highly skilled, time-intensive manual operations. This capability metamorphosis requires a critical re-evaluation of your organisation’s exposure management strategy.

Is your current approach truly equipped to withstand the speed, intricacy, and colossal volume of AI-powered cyber attacks? Let’s make sure your exposure management efforts are on the right track.

The AI-Driven Threat Evolution

AI is no longer a futuristic concept in the world of cyber crime. It’s a present reality with tangible consequences.

Threat actors are taking advantage of AI in multiple deeply disturbing ways:

• Hyper-personalised social engineering

Generative AI can craft extremely convincing phishing emails, deepfake voice messages, and video impersonations at a never-before-seen level.

And these social engineering campaigns are not your typical generic blasts. They are customised and personalised, just like in contemporary marketing, to specific individuals and roles. They take into account the nuances of the target’s internal communications, which makes them incredibly difficult to detect through traditional means.

• Automated vulnerability exploitation

AI algorithms can help adversaries automate vulnerability exploitation and the creation of exploits, as well as scan for known CVE vulnerabilities with tremendous efficiency. That minimises the time between a vulnerability’s discovery and its exploitation, drastically reducing the window for defenders to act or mitigate vulnerabilities.

• Adaptive malware and evasion

AI-powered malware is capable of learning from its environment. It can adapt its tactics to your IT environment in real time—changing its behavior, code, and delivery method—to bypass existing security controls. That means changing attack patterns, dynamically altering its signature, and evading detection by antivirus, IDS (intrusion detection), and even EDR (endpoint detection and response) systems.

• Accelerated reconnaissance

Artificial intelligence can almost effortlessly sift through volumes of OSINT (open-source intelligence) to identify key information about an organisation’s infrastructure, employees, and digital footprint. 

That’s precisely why different APT groups use AI, among others, in the recon phase of their multi-stage attacks. The purpose is for attackers to acquire as comprehensive an attack surface map as possible. 

The speed at which AI-driven attacks unfold, means that traditional and reactive security measures find it difficult to keep pace with these emerging threats. The implication is that if your exposure management strategy leans on post-breach analysis or slow, manual remediation, you’re already far behind.

The Imperative of Proactive Exposure Management

“Your organization’s security posture is an intricate ecosystem of interconnected programs, people, and processes. And exposure management is ambitiously striving to cover this entire ecosystem.” — Exposure Management Done Right: The Modern Playbook for Proactive Cybersecurity, Virsec

Exposure management, at its core, is about understanding, prioritising, and reducing the organisation’s entire attack surface. And in the age of AI-powered threats, this cybersecurity approach needs to be markedly more proactive, predictive, and dynamic than ever before.

Here are key guidelines to help you transform your exposure management strategy—if you haven’t done so already.

1. Continuous and Comprehensive Discovery

“You can’t protect what you don’t know” is a frequently repeated adage in cybersecurity, and rightly so. Cybersecurity visibility is the prerequisite for any successful cybersecurity program, and exposure management is no exception.

Continuous discovery of your assets—on-premises, cloud, APIs, IoT, third-party, and legacy software—must be the very foundation on which you build exposure management. And a complete, or at least as comprehensive as possible, asset inventory should include shadow IT as well as dormant systems and devices. 

Not only that, you should also know the vulnerabilities and different types of threat exposures, like misconfiguration or subpar access controls, along with the attack pathways to those assets.

For this reason, cybersecurity visibility should be not just continuous but automated, too. Manual asset inventories can suffer from many shortcomings, and they’re, admittedly, impractical to do. That is especially true considering the pace of changes in the contemporary threat landscape and IT environments. Hence, you should leverage automated tools that can discover and categorise assets in real time.

2. Contextualized Exposure Prioritization

Not all vulnerabilities are equally critical. Chances are that AI-powered attackers will target weaknesses that offer the greatest return on investment. Accordingly, your exposure management strategy must rest on the understanding of the real-world exploitability and potential business impact of exposures.

That requires risk scoring which considers factors like:

• Vulnerability exploitability
• Asset criticality
• Threat intelligence feeds on active exploits in your vertical
• Likelihood of a successful attack
• Possibility for vulnerability chaining
  
  

3. Proactive Validation and Simulation

The best way to truly know whether your defences are strong enough is to test them against real-world attack scenarios. Proactive offensive security is one of the landmarks of exposure management and its most recognised and advocated embodiment, CTEM (continuous threat exposure management).

Attack simulation through red teaming, penetration testing, and breach and attack simulation (BAS) can mimic the TTPs (tactics, techniques, and procedures) of AI-powered threats, allowing you to validate your controls and identify attack pathways before an actual attack occurs. These make it possible to move from a reactive security mindset to a truly proactive one.

4. Automated Remediation and Response

The speed and scale of AI-fuelled cyber attacks, or even any modern threat in general, require machine-speed defence and protection. In other words, your exposure management is only as good as its promptness.

Automated patching, configuration changes, and response should all play a part in your exposure management strategy, and an important one at that. However, focusing on prevention through autonomous workload patchless mitigation is the best action you can take to make sure you reduce your organisation’s attack surface as efficiently as possible right from the start.

For comparison, automated patching applies vendor-released software updates (patches) to fix known vulnerabilities. That is, it automates the deployment of pre-existing fixes. The speed here is in reducing the human-driven delays in testing, scheduling, and pushing out updates across an environment. Automated patching can help you deploy hundreds or thousands of patches rapidly.

However, it still depends on the availability of a patch from the vendor. Automated patching can’t help in a zero-day vulnerability attack where adversaries exploit a security weakness before the patch even exists.

In contrast, patchless mitigation has an immediate effect. It takes almost instant, automated action to prevent exploitation or stop an ongoing attack by blocking an identified threat, regardless of whether a formal patch is available.

Autonomous workload patchless mitigation doesn’t wait for a vendor patch. That is its key advantage. Moreover, since it can operate in real-time—it can take milliseconds to react to live threats—it also allows for a prompt response in addition to being preventive.

5. Leveraging AI for Defence

Threat actors use it, so why wouldn’t defenders make good use of AI? Moreover, the most effective counter to AI-powered attacks can often be AI itself.

Embrace AI-driven cybersecurity solutions to:

• Enhance threat detection: AI and Machine Learning (ML) can analyse volumes of data to identify subtle anomalies, patterns, and indicators of compromise that human analysts might miss.

• Implement predictive analytics: AI agents are capable of learning from historical data and current threat intelligence to predict potential attack vectors and exposures.

• Reduce alert fatigue: AI can help filter out false positives and prioritise alerts, reduce the workload of security professionals, and enable them to resolve the most urgent issues without risking burnout.