Cyber attackers are leveraging AI and automation to breach networks and execute lateral movements at unprecedented speeds
We’re now entering a phase where cyberattacks move faster than any human team can react. According to ReliaQuest’s 2026 Annual Threat Report, attackers are using AI and automation to expand within a compromised network in record time, sometimes in just four minutes. That’s not marginal progress; it’s a complete speed shift. The average time for attackers to move laterally between systems dropped from 48 minutes in 2024 to only 34 minutes in 2025, a 29% decrease.
This isn’t just about efficiency. It’s about control. Once attackers gain that initial foothold, AI-driven code and automated scripts let them identify and access valuable data faster and with higher accuracy than before. In practical terms, it means your team’s detection and response systems have a smaller window, minutes instead of hours, to identify what’s happening and stop it.
Executives need to think beyond traditional cybersecurity models that rely on human monitoring and manual escalation. These models are simply too slow for what’s coming next. The right strategy combines automation, AI-assisted threat detection, and continuous monitoring. This is no longer optional, it’s fundamental.
Speed is now the true advantage in cybersecurity. Attackers have it, and many defenders don’t. Decision-makers should ask whether their organizations can match this pace. Reactive security measures, even those with well-trained engineers, will inevitably fall behind. Instead, companies should focus on proactive and predictive defense systems that operate in real time. Leaders who adopt AI-driven operational security now will not only close the gap but gain a competitive advantage in risk management, operational stability, and investor confidence.
Defenders are facing a critical containment gap as manual responses become increasingly outpaced by AI-driven attacks
The containment gap between attackers and defenders isn’t theoretical, it’s measurable. The ReliaQuest report shows that organizations using AI containment tools can stop threats within four minutes on average. Teams relying on manual responses may take up to 16 hours. That’s the difference between a minor incident and a large-scale breach.
Mike McPherson, Senior Vice President of GreyMatter Operations at ReliaQuest, put it clearly: “AI and automation have changed the game in cybersecurity, allowing threat actors to move faster than any human alone can combat.” He’s right. The only viable response is for defenders to use AI with equal sophistication. ReliaQuest’s “agentic AI” approach combines automation with adaptive intelligence, enabling predictive threat containment rather than delayed reaction.
For leadership teams, the takeaway is simple. Cybersecurity isn’t only a technical challenge, it’s a strategic one. The companies that act first will set new industry standards in defense, trust, and operational efficiency. Waiting for legacy systems to evolve is no longer realistic; the gap is widening with every automated breach attempt.
Executives should view cybersecurity not just as protection but as performance optimization. AI-driven containment systems do more than block threats, they optimize workflows and reduce downtime. These systems transform security from a cost center to a driver of operational resilience. The decision is no longer whether to automate but how fast to implement automation across detection, triage, and containment workflows. In a landscape where four minutes define success or failure, speed guided by intelligence isn’t a luxury, it’s the new baseline for leadership.
Data theft and ransomware operations have accelerated dramatically
The rapid adoption of AI and automation by threat actors has changed the tempo of cyber operations. ReliaQuest’s 2026 Annual Threat Report shows that the fastest data theft event observed in 2025 took only six minutes. A year earlier, similar thefts took more than four hours. This level of acceleration means that attackers can extract sensitive data, often the prelude to extortion or ransomware demands, long before traditional security teams identify what’s happening.
The report also found that 80% of ransomware groups now use AI or automation during their attacks. By combining automated scripts and legitimate administrative tools, they can move through networks without triggering common alerts. This makes traditional defense measures less effective, since many of them rely on detecting human-like behavior rather than machine-speed operations.
For executive teams, this shift calls for an immediate reassessment of data loss prevention and encryption strategies. It is not enough to rely on post-breach containment; protection now must begin at the data layer itself. The emphasis must be on predictive monitoring, systems capable of sensing unusual access activity before data starts moving.
For decision-makers, this is a governance challenge as much as a technical one. Rapid exfiltration affects regulatory compliance, investor trust, and brand integrity. Security budgets should reflect that data is now the core asset under attack. Investments in automation, rapid isolation, and AI-driven monitoring are not discretionary expenditures, they are essential for continuity. Leaders should also ensure that incident response plans are built for sub-hour scenarios. The old assumption that teams have time to analyze, confirm, and respond is obsolete. Speed and configurability should define future resilience planning.
AI-driven reconnaissance and enhanced social engineering techniques
Attackers have become faster at understanding their targets. The report highlights that AI is being used to scan open online sources, social media, websites, and public business records, to map organizational structures and identify critical individuals. Tasks that used to take days now take minutes. That compression of time allows attackers to launch campaigns with customized messaging and high precision.
These automated reconnaissance methods enable more convincing social engineering, such as phishing and business email compromise. Because information gathering is now continuous and self-learning, attackers can automatically adjust their strategies to exploit real-time events, such as staffing changes or quarterly reports. For many organizations, this increases exposure at multiple levels, executive communications, vendor interactions, and customer-facing channels.
For C-suite leaders, that means the human element of cybersecurity cannot be treated as secondary to technical defenses. Employees and contractors remain primary targets. Awareness programs and internal communication practices must evolve to counter AI-generated deception.
This trend demands that leaders integrate human and artificial intelligence in their defensive framework. Automation can identify emerging phishing campaigns or impersonation attempts, but human judgment is essential to verify context and intent. The companies that balance these two dimensions will reduce not only breach risk but also the operational disruptions following a social engineering attack. For executives, the immediate action points are clear: reinforce identity management controls, increase employee vigilance training, and ensure your security architecture can detect AI-generated reconnaissance signatures before attacks reach your people.
Emerging malware such as BoaLoader illustrates how AI integration enhances the stealth and persistence of cyber threats
BoaLoader represents a turning point in how malware uses artificial intelligence to enhance attack quality and avoid detection. According to ReliaQuest’s 2026 Annual Threat Report, BoaLoader appeared in nearly 20% of all incidents observed in 2025, despite only emerging late in the year. It uses large language models to generate JavaScript that appears legitimate and operational, allowing it to impersonate real software such as document editors or productivity tools. Because the code is clean and well-structured, most detection systems read it as safe.
Once deployed, BoaLoader can bypass multiple layers of defense, including email gateways, sandbox environments, and endpoint detection tools. The result is a malware strain capable of remaining hidden in a network for months, gradually expanding access and persistence. ReliaQuest’s findings suggest that this type of AI-assisted threat combines technical sophistication with social deception, allowing it to gain and keep user trust.
For business leaders, the rise of BoaLoader is a sign that static defense models are no longer viable. Cybersecurity tools and processes must evolve to identify behavioral signals indicative of adaptive, machine-generated code rather than relying solely on traditional pattern matching. Detection now depends on intelligence that can identify subtle deviations in system activity, not just on known malware signatures.
For executives, the operational implication is that AI is no longer just a defensive asset, it’s now embedded in the very threats targeting organizations. Companies must enhance their detection frameworks with technologies capable of recognizing AI-produced code structures and automated behavioral shifts. Continuous monitoring at the endpoint and application level becomes essential. This is not about incremental improvement; it’s about establishing a monitoring discipline that evolves in parallel with adversarial technology. Those who invest early in adaptive detection will retain control over their data flows and operational integrity even as threat complexity grows.
ReliaQuest advocates for AI-driven defense solutions as the only viable response to match and outpace modern cyber threats
ReliaQuest’s position is clear: to survive AI-powered attacks, organizations must fight automation with automation. The company promotes its GreyMatter security operations platform as a unified system capable of merging AI-driven analysis with cross-environment data visibility. Its agentic AI, paired with a Universal Translator and detection-at-source technology, is designed to correlate threat intelligence across both cloud and on-premises infrastructures. This architecture allows faster and more predictive security responses.
Mike McPherson, Senior Vice President of GreyMatter Operations at ReliaQuest, noted that agentic AI enables organizations to “move to predictive security,” turning incident management from reactive containment into forward-looking prevention. The platform’s containment time averages four minutes, far beyond what manual teams can achieve. For organizations that still depend on traditional processes, such a performance difference highlights the scale of competitive risk tied to slow adaptation.
For executive leadership, this reinforces that cybersecurity investment is no longer a secondary decision. The evolution of attack speed changes the economics of risk. What used to be acceptable in terms of manual containment timelines is now a liability. AI-driven frameworks not only close the speed gap but also reduce operational costs through efficiency and automation across detection and containment workflows.
This is a strategic inflection point for enterprise security. Leaders should approach AI integration not as a technology upgrade but as a foundational shift in how digital infrastructure is defended. Predictive systems powered by agentic AI will define next-generation resilience, enabling faster detection, autonomous containment, and deeper visibility across connected environments. Executives who prioritize these systems will build organizations capable of operating securely at the same speed as the evolving threat landscape.
Key takeaways for leaders
- AI is accelerating cyber intrusions: Attackers now use AI to breach and move across networks in as little as four minutes. Leaders should invest in real-time, automated threat detection to counter machine-speed infiltration.
- Manual responses can’t keep pace: AI-driven containment reduces response times from 16 hours to four minutes. Executives must prioritize automation and predictive defense frameworks to close this critical containment gap.
- Data theft and ransomware escalate faster: Exfiltration now takes minutes, not hours, with 80% of ransomware groups using AI. Leaders should reinforce data security layers and deploy continuous monitoring to detect theft early.
- AI-powered reconnaissance increases precision attacks: Automated intelligence gathering enables more targeted social engineering. C-suite leaders should strengthen employee awareness programs and tighten identity and access controls.
- AI-enhanced malware is more deceptive and persistent: BoaLoader demonstrates how AI-generated code can evade detection and remain embedded for months. Decision-makers should demand adaptive detection systems that analyze behavior, not just code signatures.
- Predictive AI defense is now a business necessity: ReliaQuest’s agentic AI model proves that automation can outperform human response speed. Executives should integrate AI-driven platforms enterprise-wide to build lasting cyber resilience and operational confidence.
