Predictive Threat Intelligence: A Practical Guide to Proactive Defense

Zero-day attacks are the worst-case scenario for any security professional. When they happen, everyone looks to the cybersecurity team for a quick fix, but no patches exist yet.

If you are responsible for security in this situation, you are basically flying blind. You scramble to find, define, and solve the problem before the impact gets out of control. Stolen data, physical disruptions, and ransomware attacks not only harm your clients or employer, but they can also damage your reputation as a security expert.

Eventually, patches get developed, and the recovery process starts. However, by then, you and other zero-day victims are the characters in a cautionary tale, not the heroes of the story.

Zero-day anxiety comes from a lack of strategic solutions. Attackers have the advantage and continue to hold it until a patch gets developed. By then, it's too late for the first victims.

AI-powered attacks are increasing zero-day risks, with 74% of cybersecurity leaders saying their organisations are currently experiencing the effects of AI-powered threats. Nine in 10 think these threats will increase in the next year, and three-fifths think their current security posture will not be able to deal with these attacks. 

Enter predictive threat intelligence (PTI). PTI provides a strategic answer to this problem by changing the dynamic. Combining tools like traffic pattern detection, threat actor profiling, and vulnerability forecasting helps security professionals be proactive against threats instead of reacting to them.

Is it effective? In this article, you will learn how PTI deals with threats and see how it performed against a potentially disastrous attack.

Key Takeaways

• Traditional threat intelligence, reactive strategies, and perimeter-focused tools like firewalls are outdated and unable to deal with today's most common cybersecurity threats.
• Predictive threat intelligence offers a different solution, focused on forecasting attacks and analysing behaviours rather than looking at signatures and sources.
• A comparison of responses to the SharePoint ToolShell attacks from traditional threat intelligence and PTI shows that the latter is more effective in today's threat environment.
• The right security engine can automate threat detection and behaviour analysis and successfully remove many of the threat actors' strategies from their toolbox.

What Is Predictive Threat Intelligence?

Unlike traditional threat intelligence, which focuses on known threats like IP blacklists and malware hashes, PTI takes an offensive approach. It is a preemptive framework that reduces the danger from unexpected attacks by helping security pros prepare beforehand rather than forcing them to come up with a response under pressure after the attack has already happened.

• PTI collects data from diverse sources, such as dark web forums, network telemetry, vulnerability feeds, and OSINT. This helps define weak points that threat actors might exploit and puts new hacking strategies on security professionals' radar.
• It profiles threat actors by looking at past methods and finding patterns in how attacks were planned or carried out. It can then detect these patterns in the future to provide early warning of a possible attack.
• Robust AI/ML models monitor networks for unusual activity and flag emerging threats before they fully materialise.
• These models can also be used internally to predict vulnerabilities and suggest preemptive patches.

PTI taps into the wealth of available cybersecurity data and uses ML and AI to develop proactive strategies and data-based forecasts to help security teams prepare for the next wave of threats.

The Critical Shift: From Reactive Signatures to Proactive Defence

Traditional threat intelligence is always a step behind. Why? It deals with known signatures. Security teams using this strategy will see clear indicators of compromise (IOCs) that tell them an attack is taking place.

This approach is effective against known IOCs, but it's completely blind when it comes to zero-day attacks. Also, the trend of using AI to generate unique code for each attack is quickly making traditional signature-based approaches obsolete.

Because it doesn't rely on static patterns or existing signatures, PTI does not require security teams to play the constant game of catch-up.

How a Modern Predictive System Works

What technologies power this protective strategy?

PTI's effectiveness comes from its diversity of data sources. Bringing this information into a central repository and analysing it paints a complete picture of vulnerabilities and threats. This is extremely useful for modern predictive cybersecurity systems with the tools to analyse this data effectively.

AI/ML engines collect and analyse this data to find patterns. Once detected, they can project the trends into the future to create forecasts and attacker profiles. Pattern recognition is a key component of PTI, and manual analysis of the amount of data needed for effective predictions is simply impossible.

Behavioural analytics tools learn patterns of networks and databases and then detect anomalies in real time. These tools can detect an abnormal use of permissions, unexpected code, or suspicious movements of data. This detection can be an early warning sign of an attack. This allows for preemptive measures to stop a problem before it starts.

Proof in Action: Inside the SharePoint 'ToolShell' Zero-Day Attack

In theory, predictive threat intelligence deals with the problems plaguing signature-based cybersecurity strategies. As with any newer approach, there's a level of uncertainty. Some pros might think that betting on unfamiliar technology is riskier than using known strategies, even if those known strategies aren't always effective.

How does PTI perform when confronted with a real-world threat? This predictive threat intelligence case study shows you how. 

The Nothreat PTI platform faced a very real threat from the SharePoint ToolShell zero-day attack. It effectively gave cybersecurity teams the warning they needed to preemptively thwart the incursion.

Here is how the narrative played out.

The Anatomy of the Attack

Microsoft SharePoint is an important tool for innumerable businesses and organisations. Because it's used to store, organise, and share data and files, it's also a tempting target for bad actors.

ToolShell is the name given to the exploit chain that targets two common vulnerabilities and exposures (CVEs) in SharePoint. It provides access to SharePoint servers using unauthenticated remote code execution (RCE). 

The first exploit chains related to ToolShell were detected in April 2025, with the specific CVEs exploited as early as 7 July, well before zero day on 19 July , when Microsoft officially acknowledged the CVEs. It took two to three additional days for full patches to be released. 

How do threat actors use the vulnerabilities? CVE-2025-53771 allows hackers to bypass authentication. They then exploit CVE-2025-53770, an RCE vulnerability that allows the deserialisation of insecure data. CVE-2025-53770 allows attackers to submit malicious code, drop a web shell, and steal cryptographic keys. Hackers already had a foothold on SharePoint thanks to previous CVEs. They were able to circumvent patches meant to shore up these weak points.

Exploiting each of these vulnerabilities by itself would not lead to a catastrophe. However, by exploiting them together, threat actors have the ability to carry out a serious attack and maintain persistent access even if one vulnerability gets patched. Traditional security systems could not detect these chained vulnerabilities.

Why Traditional Defences Were Blind

Why was ToolShell so dangerous? No signatures existed. Traditional defence systems like firewalls, signature-based AV, and legacy IDS/IPS weren't able to detect or stop the attacks.

Instead of forcing their way into the system, threat actors used normal SharePoint functions to gain access. For instance, the first step of the attack relied on referer header spoofing to get entry. The attackers then exploited the deserialisation vulnerability to drop serialisation payloads. Because these naturally change structure and encoding, they went undetected by traditional signature-matching tools.

Once they had access, attackers took encryption keys and were able to forge tokens that traditional security systems saw as legitimate.

ToolShell highlights how traditional approaches focus on perimeter defences and signatures. Once inside, attackers were able to use keys to persistently access data without any detection.

The Predictive Block: How Nothreat's Autonomous AI Responded

PTI does not rely on IP addresses, signatures, or the detection of forced log-in attempts. Here's an example of why PTI is effective in such situations.

Nothreat was able to detect and neutralise the threat for a major telecom company operating a nationwide private network.

Nothreat's AI/ML engine does not rely on signatures or the reputation of the source. Therefore, it didn't fall for the spoofing activity that served as the first step in a ToolShell attack. Instead, the engine analysed the patterns of the requests themselves and detected potentially malicious activity.

Nothreat's system autonomously blocked the threat without any human intervention due to this pattern recognition. Because such actions are a standard part of the engine's daily activities, no one was even aware that the system had blocked a zero-day attack until the CVEs ToolShell sought to exploit was publicly announced.

This shows how AI/ML platforms like Nothreat offer a plug-and-forget PTI solution.

The Result: 40 Days of Proactive Protection

Nothreat's predictive cyber threat protection action happened a full 40 days before the CVEs related to ToolShell were publicly announced.

Nothreat's telecom client experienced no data loss and zero downtime due to the ToolShell attack. The security team did not have to scramble to respond to the attack. Instead, they continued their normal activities without panic or disruption. This was even the case after Microsoft released four separate patches.

The client remained fully protected throughout the 40-day period, and their operations were not interrupted at all. Other organisations were not so lucky. A total of 396 compromised systems were linked to the ToolShell zero-day vulnerabilities. A third of the victims were government agencies, including the US Departments of Homeland Security and Health and Human Services. Because of the nature of the attack, problems like stolen data are likely in many of these cases, and some ransomware attacks have been linked to the ToolShell breaches.

Key Principles for Building a Proactive Security Posture

It's clear from Nothreat's success in dealing with ToolShell attacks that a proactive security posture is essential for dealing with today's cybersecurity environment. Here are three key mindsets to help security professionals embrace this new way of thinking.

• Embrace automation: Companies can choose an AI/ML-powered security engine like Nothreat to automatically detect and block attacks. With such a system in place, the human team do not have to get involved unless it's absolutely necessary.
• Adopt a zero-trust approach: Security professionals need to have tools to continuously monitor traffic regardless of where it comes from. With this approach, security experts can automatically deal with threats like spoofing, stolen credentials, and other common attack strategies. 
• Diversify data: A security team needs to set up a system that draws information from network telemetry, dark web forums, open-source intelligence, vulnerability logs, and any other relevant sources. This gives the team diverse data to make more accurate predictions.

These require having the right tools in place to provide comprehensive protection.

Evaluating a Predictive Security Solution: What to Look For

Obviously, the quality of tools and features is the bridge between theory and real-world performance.

What do these security solutions need to have to ensure positive outcomes in the face of zero-day threats?

• Diverse data sources: A complete picture and accurate predictions require data from everything from social media and the dark web to OSINT and internal network telemetry. An effective predictive security solution should draw from all these sources, and more.
• Proven autonomy: The system should rely on AI and ML for pattern recognition and behaviour analysis. These tools work autonomously, so they can handle the data and monitoring in real time without requiring hours of intense manual work.
• Threat intelligence: A system should keep and update data on threat actors and use it to create profiles and activity patterns to help predict the timing of future attack attempts.
• Actionable intelligence: Predictions should include information about potential vulnerabilities or access points. This allows teams to make patches or monitor specific areas before the problem arises.

Finally, proven real-world results are essential for measuring a system's effectiveness. For instance, Nothreat's success in the ToolShell situation shows that it is effective against real-world threats. With such proof, you can be sure that a platform has successfully put PTI theories into practice.

Conclusion

Traditional threat intelligence is no longer effective in today's threat landscape. Predictive threat response can take advantage of the wealth of data available to level the playing field for SecOps and CISO professionals. With the right platform and tools, PTI can put you in a position to neutralise zero-day attacks and unknown threats before they cause damage.

Theory is important, but proven protection, as you can see from the SharePoint ToolShell zero-day case study, is what matters.