Can AI Predict And Prevent Cybersecurity Threats Before They Hit? The pre-emptive approach pretty much aligns with the changing norms in the cybersecurity regime wherein it's transitioning from detection to prediction.

For the longest time, conventional firewall systems have helped companies keep cyber threats in check. But they have evidently not been enough given several major companies have fallen prey to serious attacks through the years.

Back in 2017, a ransomware called WannaCry wreaked havoc worldwide by targeting computers running Microsoft Windows operating systems, encrypting data, and seeking ransom payments in the form of bitcoins. Reported in the same year, Yahoo! apparently suffered a major data breach in 2013–14 affecting all users. It's also considered to be one of the biggest cyber attacks in history. There are several examples of ransomware and malware targeting companies of all sizes in the last decade or so.

While these firewall systems have evolved too, they have been mostly reactive, and now that AI is there, is it possible for companies to leverage the new tech to preemptively plug the gaps? The preemptive approach pretty much aligns with the changing norms in the cybersecurity regime wherein it's transitioning from detection to prediction.

Moreover, stakes have always remained high for businesses. According to an IBM study, the global average cost of data breach had hit USD 4.88 million as of 2024. This is roughly 10% up from the previous year and the highest increase since the pandemic.

"A rise in the cost of lost business, including operational downtime and lost customers, and the cost of post-breach responses, such as staffing customer service help desks and paying higher regulatory fines, drove this increase. Taken together, these costs totaled USD 2.8 million, the highest combined amount for lost business and post-breach activities over the past 6 years," the IBM study noted.

Even as cyber attacks become more sophisticated, the economic realities are now forcing businesses to consider just another cost center but also as an investment.

The IBM study noted that an increasing number of companies are now deploying security AI and automation as well as saving a lot of money as well.

"2 out of 3 organizations studied stated they're deploying security AI and automation across their security operations center, a 10% jump from the prior year. When deployed extensively across prevention workflows—attack surface management (ASM), redteaming and posture management—organizations averaged USD 2.2 million less in breach costs compared to those with no AI use in prevention workflows. This finding was the largest cost savings revealed in the 2024 report," it noted.

From Detection To Prediction

One of the greatest shifts, as mentioned above, is towards predictive analytics. With AI, it has moved from reactive to proactive as now it enables the firms to connect the dots at scale instead of scanning isolated alerts. Moreover, modern AI engines are capable of identifying and surfacing weak signals to show that an attack is taking shape.

"This is used to move clients from log-watching to hypothesis-driven defence, mapping likely attack paths, high-value assets, and choke points. Combined with graph analytics and continuous learning, AI can flag a risky privilege change or lateral move long before a signature exists. It is less about one magic algorithm and more about an operating model where AI is embedded into our GRC, threat, vulnerability and identity services. That is what turns AI from a clever detection layer into a preventative fabric that quietly hardens the environment every day," Deep Chanda, Chief Officer of Ampcus Cyber, explained.

Vivek Bajpai, Co-founder & CTO at GoKwik explains that by analyzing user behavior data, network patterns, and massive threat-intelligence feeds, AI can spot anomalies and predict attacks such as account takeovers or unusual activity before they escalate into serious threats.

"Classical machine learning and deep learning on large datasets enable AI systems to detect abnormalities, correlate suspicious activities, and flag unusual access patterns in near real time," Bajpai added.

Building Trust, Humans, And more

Another advantage that the move to AI brings is addressing the human capital issue. It is estimated that the global cybersecurity workforce gap is approximately 4.8 million professionals, according to the 2024 (ISC) Cybersecurity Workforce Study. It's unlikely that the companies are going to double down on hirings.

"AI will play a key role in closing the cybersecurity skills gap by automating high-volume, repetitive, and complex tasks. In the SOC, AI can sort alerts, connect different data sources, enhance incidents, and carry out preset containment steps. This can significantly reduce analyst fatigue. In compliance and GRC functions, AI can outline controls, track policy compliance, and automate evidence gathering, freeing teams from manual tasks. This enables limited human expertise to concentrate on threat hunting, in-depth forensics, and strategic risk reduction. When paired with effective data integration, continuous validation, and ongoing employee training, AI acts as a force that boosts, not replaces, human ability." Chanda explained.

As far as trust goes, experts believe AI is capable of handling heavy operational load, enabling human analysts to focus on even more complex challenges relating to security.

"Building trust requires AI systems that are designed to be transparent and accountable. This means every alert or action should come with a clear explanation, linking decisions to behaviors, deviations, or threat patterns instead of unclear risk scores. We need to make decision lineage visible: data sources, features, confidence levels, and possible alternative interpretations. Improving outputs with asset context, identity relationships, and attack-path insights helps analysts understand right away why it matters. Continuous validation, drift monitoring, and open documentation ensure analysts see how models change over time. Including human-in-the-loop controls, where analysts can confirm, challenge, or refine decisions, creates a cycle that strengthens both accuracy and confidence in AI-driven defense," he added.

Khushhal Kaushik, Founder & CEO, Lisianthus Tech says that for the first time, security models built specifically for cyber defense, trained on vast, multi-source telemetry, are capable of understanding intent, mapping entire attack paths, and flagging threats before they materialize. This fundamentally changes the economics of defense. When AI can automate up to 80% of repetitive SOC functions, cut false positives to a fraction, and shrink detection windows from days to minutes, human analysts are freed to focus on the high-judgment problems only humans can solve.

"To build analyst trust, AI systems must shift from "black box scoring" to "glass box reasoning." That means exposing which features drove a decision (for example, unusual login geography, new device fingerprint, abnormal data volume), surfacing comparable historical incidents, and providing plain-language rationales alongside risk scores. Research in explainable AI (XAI) for cybersecurity is already showing that techniques like feature attribution, rule extraction and counterfactual explanations can be embedded directly into SOC workflows. Transparency is not just a UI feature but it's a full feedback loop where human expertise continuously recalibrates AI judgement," Vivek Chandran , Co-founder, Risknox explained.

Plan Of Action

Experts are pretty much on board with the fact that the transition to predictive defence is the need of the hour. AI is now pretty much the headline in the cybersecurity strategy for all major firms. In a newer environment, however, businesses must focus on data quality, keep biases in check and better governance, as well as experts for efficient human-machine interactions. Also, they need to focus on building privacy as a non-negotiable element, especially in sensitive sectors such as HR, finance, and health.

"The long-term vision is a mature, collaborative model where AI provides constant scale and accuracy, while humans offer strategic judgment, ethical oversight, contextual analysis and creative problem-solving. Autonomous systems will continuously watch signals, correlate patterns, isolate suspicious activity and take bounded actions such as killing a process or revoking a token. Human experts will decide the rules of engagement: what "safe" looks like, which trade-offs are acceptable and how to respond when business and security interests collide," Chanda said.