Attack surface management, or ASM, has long been a critical component for effective cybersecurity, but it’s never been more challenging. We’re talking about monitoring every possible touchpoint that malicious actors could use to infiltrate your ecosystem, so you can respond to attacks rapidly and either mitigate their effects or (ideally) prevent them from occurring. 

The complexity of ASM is directly proportional to the complexity of your attack surface, and that’s grown significantly over the last few years. IoT extended attack surfaces to include masses of devices, while the remote work phenomenon introduced employees’ personal devices and home networks which don’t always enjoy enterprise-level protection. Cloud adoption means that your attack surface could be hosted by third parties, and their defenses may be outside of your control. 

The good news is that artificial intelligence is here to help. AI technologies are revolutionizing attack surface management by automating monitoring, alerts, and response activities, offering smarter, faster, and more accurate insights into vulnerabilities. With earlier alerts, you can address potential issues before they escalate into serious threats. 

In this article, we’ll explore how AI-powered automated attack surface management helps to reduce risks, increase accuracy, and equip organizations to manage their cybersecurity posture more effectively. 

Understanding Attack Surface Management

Let’s begin with a closer look at what goes into attack surface management. ASM involves identifying each element that makes up your attack surface; monitoring activity around every point that attackers could exploit to gain unauthorized access to your systems; and taking steps to close security gaps and quickly remediate or mitigate attempted attacks. 

There are four distinct steps in successful attack surface management:

  • Asset discovery. This involves identifying and understanding every part of your attack surface, including components like IoT devices, remote work networks, third-party integrations, and cloud services. 
  • Risk assessment. In this step, you’ll evaluate every item that makes up your attack surface, probing all the vulnerabilities, misconfigurations, and other potential threats so as to determine the security posture of every asset.  
  • Continuous monitoring. It’s important to set up mechanisms to constantly monitor all your assets and network activity and deliver real-time alerts that notify you immediately that there’s an emerging threat, policy violation, or suspicious anomaly. 
  • Incident response. The final step in any ASM strategy involves responding as quickly as possible to security incidents, so that you can minimize the damage, reduce the impact, and restore normal operations as fast as you can. 

Challenges in Attack Surface Management

As mentioned above, attack surface management has only become more difficult in recent times. For a start, attack surfaces are more complex than ever before. They are constantly growing, with dynamic new environments such as cloud, IoT, and remote work, which makes it harder to track assets. 

Shadow IT is also an increasing problem. The ease of adopting SaaS solutions results in employees introducing new vendors, software, and platforms that security teams not only haven’t approved, but aren’t aware of, making it impossible for them to track them effectively. 

Third-party risks obscure visibility even more. Your vendors, service providers, partners, and other members of your supply chain introduce vulnerabilities that are outside of your control, and so do their third-parties and their third-parties. Frequent updates, deployments, and changes to configurations continuously open up new security gaps. 

At the same time, resources are limited. There’s a persistent lack of skilled security personnel and risk management experts, with the result that existing teams are overstretched. They can’t keep up with the whackamole of attack surface management, and are overwhelmed by alerts that seem to never stop coming. When they struggle to prioritize real threats, the biggest dangers can slip under the radar — until they escalate into major security incidents. 

The Role of Automation and AI in Cybersecurity

AI is well suited to deliver value for ASM. AI, and its subset machine learning (ML), collect and crunch massive amounts of data in real time, delivering seamless monitoring that is more constant and comprehensive than any human security team could ever achieve. With automated AI cybersecurity, you can scale up ASM effortlessly, for systems of any size, without stressing your teams or running out of resources. 

These tools recognize the patterns that constitute “normal” behavior and spot anomalies that could indicate a security threat, triggering alerts that are both more accurate and more timely. AI smart solutions can take it a step further and analyze emerging risks to prioritize those that need immediate attention and ensure that they don’t slip between the cracks. 

What’s more, AI and ML tools can predict potential threats and address them independently. Automating incident response ensures that threats are mitigated and neutralized as soon as possible, even if they arise when no human is around. Automation also frees security personnel from tedious repetitive tasks, helping avoid burnout and allowing them to focus on more strategic work. 

How Automation Enhances Attack Surface Management

Automation can be transformative for attack surface management for a number of reasons:

  • Asset discovery is more reliable and swift with ML-powered analysis makes a, ensuring that no item goes unnoticed;
  • Risk prioritization can be carried out more quickly and accurately, so that nothing falls between the cracks;
  • Automated continuous monitoring and threat detection never loses focus or misses an event;
  • Predictive analytics shifts ASM from reactive to proactive to prevent risks from escalating;
  • Incident response automation ensures that every threat is addressed immediately.

Asset Discovery and Mapping

AI-powered automation brings significant benefits for the tasks of identifying and assessing assets within your attack surface. These tools can scan your ecosystem to spot every hardware or software, whether it’s on-prem or cloud-based, including IoT devices, remote working networks, and SaaS platforms. 

AI and ML shine a light into shadow IT to create a comprehensive, reliable map of every asset. Because they are automated, they can scan and rescan all your systems continuously, picking up every new asset as soon as it’s added. What’s more, AI and ML asset discovery can understand the nature of the elements they find and categorize them accurately, creating easy-to-read guides to your ecosystem. 

Risk Prioritization

Automation is just as valuable for the next step of risk prioritization, which ensures that the most serious risks are addressed first. AI and ML algorithms can analyze large datasets from your security monitoring tools, and quickly rank risks based on the threat they post to your organization.  

Effective risk prioritization helps allocate resources more efficiently, and directs incident response actions and security teams to focus on vulnerabilities according to their risk level and potential impact. This way, your security personnel will put their time and energy to better use. Instead of wasting time on low-priority tasks that could wait for a slow day, they’ll always begin with the most critical issues that require the most attention. 

Continuous Monitoring and Threat Detection

Robust, accurate continuous monitoring is almost impossible to achieve without AI. Until the advent of automated systems, monitoring and threat detection relied on rule-based and signature-based systems, log reviews, and human surveillance. Vulnerability scans only ran periodically, and actions like user behavior analytics relied on statistical models which quickly fall out of date. 

Manual and intermittent monitoring isn’t able to deliver real-time, reliable alerts about anomalies and suspicious activities across the attack surface. Automation ensures that every potential threat is spotted and addressed, leaving no risk lurking hidden in the sprawling attack surface. This reduces the time it takes to detect and respond to potential breaches, preventing them from escalating into serious incidents. 

Predictive Analytics

Predictive analytics utilizes AI and ML automation to make attack surface management even more powerful. This involves analyzing historical attack data to identify patterns that indicate future vulnerabilities and attack vectors, helping you shift from reactive to proactive security.. 

Together with automated continuous monitoring, threat detection, and risk prioritization, this makes it possible to implement measures that close security gaps before malicious actors can find and exploit them. By enabling proactive risk mitigation and vulnerability patching, predictive analytics helps you to stop chasing fires and reduce your overall attack surface. 

Incident Response Automation

Last but not least, AI and ML can automate incident response after they detect a serious threat. You can predefine various actions to take in response to threats, such as isolating compromised systems, applying software patches to close vulnerabilities, and removing access credentials from users who may have been hacked or could be abusing their privileges. 

Automated incident response can significantly shorten the time it takes to address threats and attacks. Instead of waiting for a human security member to notice an alert, decide what action to take, and authorize that action, remediation and mitigation can take place instantly. Additionally, it lightens the workload for security teams, who don’t have to interrupt other work to put out a fire, and are free to focus on more complex and strategic tasks. 

The Intersection of Attack Surface Management, Automation, and Third-Party Risk Management

Effective cybersecurity has to be holistic. You need to secure every element of your ecosystem in order to protect your organization from malicious actors, but every step you take to strengthen cybersecurity in one area has a positive effect on other areas. 

In practical terms, this means that improving your attack surface management also raises the standards for third-party risk management (TPRM), and vice versa. Your third-party vendors, partners, and service providers all contribute to your attack surface, making it more extensive, complex, and murky. 

But the same AI-powered automation that removes friction from ASM and makes it more accurate and friction-free also opens up visibility into third-party risks, enables reliable third-party threat detection and risk prioritization, supports continuous monitoring for third-party risks, and speeds up incident response to third-party breaches. 

Most good AI automated solutions meet both third-party risk management and attack surface management needs, helping bolster your organization’s security posture from multiple angles. 

Automated Attack Surface Management Solutions 

Introducing automated attack surface management is a smart move for cybersecurity. By automating processes like asset discovery, continuous monitoring, threat detection, and incident response; improving accuracy for alerts; and enabling proactive risk management, AI and ML take ASM to the next level. 

Overall, automated attack surface management helps shorten time to mitigation or resolution, and prevent malicious actors from finding and exploiting vulnerabilities. At the same time, it lowers the burden on security teams, helping prevent burnout and freeing them to focus on strategic planning. 

Without integrating AI into ASM, enterprises will struggle to secure their expanding, and increasingly complex, attack surfaces. Smart organizations are already exploring automated attack surface management solutions like Panorays, to investigate the best ways to introduce them into their tech stack and maintain a robust cybersecurity posture. 

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