Today’s headphones can translate spoken languages in real time, deepfakes and AI generated content are getting indistinguishable from reality, and driverless 18-wheelers are shuttling goods across North American highways. Just like the rest of the digital world, cybercriminals are leveling up in creative ways – 80% of ransomware attacks now use artificial intelligence, allowing attackers to hack at unprecedented speed and scale. Even voice phishing (vishing) is on track to double in volume.
What’s worse, today’s hackers aren’t hooded lone wolves behind keyboards; they’re orchestrating multi-vector attacks that are choreographed like a shape-shifting bank heist and then, they’re layering new AI capabilities on top of these attacks to remain undetected.
In a bank heist scenario, one criminal tunnels to the vault underground, another drops in through the skylight, while a third strolls through the lobby flashing a flawless fake ID. Their AI capabilities make them increasingly, imperceptibly dangerous, allowing them to instantly swap disguises if guards get suspicious, adapt to alarms the moment they sound, and coordinate automatically with each other without ever speaking out loud.
How do we protect our networks and data vaults from modern multi-vector, AI-enhanced attackers? It takes more than static segmentation and perimeter firewalls – defenders need a living tapestry of controls to outmaneuver evolving tactics.
Three Multi-Vector, AI-Enhanced Attacks to Watch
Modern attackers are blending techniques and leveraging AI to automate attack research and execution, adapt to new environments and defenses, and escalate operations at unprecedented speed. AI compresses months of manual work into hours, enabling campaigns that can hit thousands of targets in parallel.
Stephanie Carruthers, the Chief People Hacker for IBM X-Force Red, has been testing and quantifying just how much AI is able to help hackers: “With only five simple prompts we were able to trick a generative AI model to develop highly convincing phishing emails in just five minutes, the same time it takes me to brew a cup of coffee. It generally takes my team about 16 hours to build a phishing email … So, attackers can potentially save nearly two days of work by using generative AI models.”
Campaigns go well beyond “smarter phishing,” combining hyper-targeted social engineering, infrastructure mapping, rapid exploitation, and machine-speed automation. Security leaders across industries are reporting increasingly layered and dynamic attacks in today’s AI-enabled threat landscape. Let’s dive into a few examples.
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AI-Powered Phishing → Credential Abuse → Lateral Movement
- Attackers use AI to generate convincing social engineering attacks like spear-phishing emails, texts, or human-like voice calls to targeted users.
- Once stolen, the credentials are instantly validated using AI credential stuffing.
- Attackers gain a foothold, then move laterally, impersonating legitimate users and escalating privileges until they hit the jackpot – the network’s crown jewels.
Attack Chain: Social engineering → Identity theft → Lateral movement
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Ransomware 2.0: Exploit → Asset Discovery → Encryption & Exfiltration
- Attackers start with phishing or a vulnerability exploit discovered via AI reconnaissance.
- AI pinpoints high-value targets and moves laterally to reach them – such as backups, domain controllers, and sensitive data. What’s worse, they can even pivot through segmented networks by probing for misconfigurations, weak firewall rules, or vulnerable systems that bridge multiple VLANs (like backup servers, monitoring tools, or legacy applications).
- The AI then initiates both data encryption and stealthy exfiltration.
Attack Chain: Initial access → Rapid asset mapping → Unencumbered lateral movement → Payload/drop
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Supply Chain Breach → Rapid Network Mapping → Custom Malware
- Attackers compromise a vendor or MSP, piggybacking into their customers’ networks.
- AI automates network reconnaissance, quickly mapping vulnerabilities and moving laterally to reach them.
- AI then deploys polymorphic malware that customizes itself for each target environment, adapting to the victim’s network to evade detection by security tools.
Attack Chain: Third-party compromise → Automated network mapping and lateral movement → Tailored exploit delivery
Segmentation Alone Isn’t Enough to Stop Advanced Attacks
45% of cyber professionals say their organization is not adequately prepared for AI threats, and 78% of CISOs say that AI powered threats are already having a significant impact on their organization. Only 50% of cyber pros have confidence in traditional cybersecurity tools to detect and block these modern attacks. They’re right – traditional network segmentation isn’t enough to contain attackers in the age of AI. Today’s adversaries aren’t pushing through one static barrier – they adapt, reroute, and link together multiple techniques until they find a way in.
And they don’t stop at the first barrier or firewall. They push through multiple vectors and AI allows them to adapt when a path is blocked. What looks like a failed mishing or phishing attempt may be a distraction, pulling attention away while automated malware pinpoints misconfigured VLAN rules or dormant service accounts that serve as a pathway across the network.
Even when segmentation is in place, attackers use AI to map the environment almost instantly, identifying gaps in security where they can silently bypass firewall rules. Then, once inside, they don’t just move laterally, they move intelligently – disguising behaviors to look like normal activity, attackers evade EDRs and alerts as they unlock the crown jewels.
Segmentation can slow attackers down, but on its own, it doesn’t prevent lateral movement. To effectively contain and neutralize these campaigns, segmentation has to evolve.
Containing Threats With Identity-Aware Microsegmentation
Defenders need more than static segmentation and perimeter firewalls. Networks must be divided into smaller zones to limit lateral movement – but that’s only the first step. True resilience requires microsegmentation that’s adaptive, identity-aware, and continuously enforced in real time.
A study published in the International Journal of Computer Applications and Research validates this approach. The research into Zero Trust Enforcement Using Microsegmentation shows that identity verification must be combined with contextual signals such as device posture, location, and behavior. That means every connection attempt is evaluated dynamically by policy engines driven by live telemetry, ensuring security isn’t bound to brittle VLANs or static IP ranges but instead travels with the identity across hybrid data centers, cloud workloads, and legacy systems.
Layering identity segmentation on top of granular network policies ties access directly to users, devices, and service accounts. This ensures policies follow those identities everywhere – even if credentials are stolen.
Finally, defenders should enforce just-in-time MFA at the network layer (tied to Layer 3) to protect every port and workload, not just SaaS apps. Research backs this, noting that multi-factor authentication combined with dynamic, risk-based controls is critical to preventing stealthy pivots. This turns what used to be invisible lateral steps into instantly contained dead ends.
Together, identity-aware microsegmentation and just-in-time MFA transform segmentation from a static control into a living defense fabric: adaptive, context-driven, and resilient enough to contain chained, AI-powered attacks that legacy approaches can’t stop.
