Last year was a turning poi for the developme of artificial ielligence, and its consequences covered almost all areas of business and life. At the same time, cyber attacks have leapt in number and sophistication, bypassing legacy security systems and causing unprecedeed damage. These trends will accelerate in the coming years, and according to Scott Harrell, CEO of Infoblocks, the only solution is to transition from reactive to proactive cybersecurity in 2026.
Experts are reportedly warning that relying on traditional “detect and respond” models is no longer enough. Organizations must redesign their security architecture, aicipate attacks, and optimize security team workflows to couer AI-based threats. Harrell emphasizes that the security model of a pre-AI world will not work against AI-equipped attackers, and proactive cyber security will become a strategic imperative in 2026.
3 trends that will determine the future of cyber security in 2026
The first importa trend Transforming the threat landscape is In 2026, the mass personalization of cyber attacks will disrupt the classic model of the kill chain, which relies on observation and then reaction. Attackers use artificial ielligence to analyze the specific vulnerabilities of each business and produce specific malware for each organization. The result is a dramatic jump in the number of sophisticated attacks tailored to each target; Attacks that many curre security tools don’t recognize, putting organizations in a race against time to detect and coain them before widespread damage occurs.
Adding artificial ielligence to existing reactive tools helps to some exte, but alone is not the answer to this new wave. Harrell explains that security teams must devise eirely new approaches to proactively mitigate risk and bypass personalized attack paths. This approach includes coinuous behavior analysis, attack scenario modeling, and defense decision-making automation to block the attack chain before it is activated.
Artificial ielligence also gives rise to a new generation of automated and flexible malware. These malwares can change their code and behavior to stay hidden from defense systems. This ability makes it less likely that new attacks will be detected before they cause extensive damage. Automated malware based on artificial ielligence marks a new stage in cyber threats; A stage where smart, resilie and constaly evolving attacks put more pressure on tools that rely on the detection and response model.
At the same time, the issue of deepfake is seriously aggravated. Advances in generative artificial ielligence make creating fake videos and audio files simple and cheap. These coes are used to spread false information, deceive employees and managers, and impleme social engineering attacks, increasing the probability of successful fraud and theft. The combination of deepfake with the new generation of email, SMS and social network attacks that are personalized with artificial ielligence for each person completes the chain of deception.
These messages are very similar to real communication in terms of tone, writing style and timing and are difficult for humans to recognize. Relying on the user as the last line of defense against such threats collapses. Harrell emphasizes that modern security requires automated and adaptive defenses; A defense that removes the burden of diagnosis and decision making from people and reduces human error.
The second process Expansion of the attack surface (Attack Surface). IoT devices and IT equipme such as routers, switches and network security systems are becoming more attractive targets for attackers. It becomes easier to produce and impleme attacks against these devices, and their number is increasing in work and home environmes. By infiltrating these pois, attackers gain a foothold and use it to navigate the network, disrupt operations, and cause damage.
Network infrastructure and legacy or custom security mechanisms are also at greater risk. Artificial ielligence allows attackers to quickly adapt attacks to differe operating systems and software versions. This ability reduces the cost and time of developing malware for various platforms and makes attacking these infrastructures more economically attractive. The result is an increase in security incides and irusions that originate from pois that were previously thought to be low-risk.
Artificial ielligence itself becomes one of the most attractive parts of the attack surface. With the expansion of the use of artificial ielligence models in eerprise software, these systems have wide access to sensitive data and critical processes. Harrell warns that attackers can manipulate the automated nature of these systems to act like a human insider threat. High-level access of iernal models, if misused, leads to massive data leakage and disruption of business processes.
To curb this risk, organizations must take AI layer security seriously. This work includes strict corol of models’ access to data, coinuous monitoring of input and output, protection against manipulation of training data and definition of transpare policies for the use of artificial ielligence in sensitive processes. Without these measures, the AI-related attack surface will quickly spiral out of corol.
The third trend, maturity of the modelCyber crime service” is The era in which the attacker’s technical prowess limited the scope of the threat is over. Today, the underground economy based on artificial ielligence has transformed the threat landscape and given unprecedeed power to wealthy actors. Without deep technical knowledge, these individuals have access to an array of ready-made services such as exploit kits, ransomware-as-a-service platforms, ideity marketplaces, and early access brokers.
According to Harrell, by 2026 the cybercrime service procureme model will reach a new level of sophistication. Artificial ielligence tools allow inexperienced attackers to execute multi-stage and complex campaigns with high accuracy. The line between opportunistic hackers and organized cybercrime gangs is blurring, and the scale and sophistication of for-profit attacks is reaching an unprecedeed level.
Harrell believes that in such circumstances, adding artificial ielligence to old tools only creates an “illusion of security.” This illusion will be painfully revealed in 2026; When organizations realize that their seemingly iellige systems are ineffective against attackers equipped with artificial ielligence. He emphasizes that proactive cyber security in 2026 means transitioning from a reactive position to a strategy that aicipates an attack and blocks its path in advance.
This transition requires investme in automation of the defense sector, predictive analysis, design of irusion-resista architectures, and coinuous training of security teams. Organizations that take this change seriously have a better chance of being resilie against the new wave of AI-based threats. Other organizations face the risk of widespread attacks and heavy losses in the coming years.
