Over the past few days, one question has consistently come up in my conversations with clients: “What do you offer in AI for security and security for AI, and how can it benefit our organization?” While the question appears simple, the answer is far from a one-liner.
As the head of cybersecurity, I recognize that AI cannot be viewed in a single dimension. It demands a holistic approach – one that enables innovation while proactively managing risks, ensuring that we do not introduce new vulnerabilities or inadvertently expand the attack surface. The goal is not just to meet expectations, but to stay ahead of them.
The AI inflection point in cybersecurity
Cybersecurity is evolving at an unprecedented pace, and AI is at the center of this transformation. What started as a productivity enhancer has now become a core business enabler, driving automation, accelerating decision-making, and improving operational efficiency.
Advanced AI platforms, such as Mythos AI and similar engines, are empowering enterprises to unlock new capabilities. However, they also introduce a critical and pressing concern that every CISO is raising today: “What happens if these powerful AI tools fall into the wrong hands?”
The reality is stark – AI does not just enhance traditional cyberattacks; it fundamentally redefines them.
Attackers are now leveraging AI to execute:
Highly personalized phishing campaigns
Deepfake-based impersonation and fraud
Automated reconnaissance and misinformation
Synthetic identities and intelligent malware
In essence, AI is enabling faster, scalable, and highly adaptive cyberattacks that target both technology and human trust.
Leveraging SOC as the control tower for AI threats
To combat these advanced threats, enterprises must evolve their security operations center (SOC) into an intelligence-driven, automation-enabled defense platform. The SOC becomes the “eyes and ears” of enterprise security, providing centralized visibility, correlation, and response across identity, endpoint, network, OT, IoT, and cloud environments.
Below are key SOC use cases that organizations should implement to effectively detect, investigate, and respond to AI-driven (including Mythos-like) attacks:
1. AI-powered phishing detection
Research from Microsoft says that 80% of enterprises experienced AI-assisted phishing attempts in the past year. Therefore, it is recommended that enterprises develop SOC use cases to detect highly personalized phishing attacks generated by AI or large language models by monitoring indicators such as anomalous sender locations, newly registered domains, and suspicious language-similarity patterns.
2. Deepfake identity and voice fraud detection
A 2025 Gartner survey found that 62% of enterprises experienced a deep fake attack involving social engineering, executive impersonation, and automated fraud workflows. It is recommended to develop SOC detection use cases focused on suspicious authentication and impersonation attempts, including monitoring unusual executive login activities, abnormal Teams or VoIP usage, impossible travel patterns, and potential MFA bypass scenarios.
3. Insider threat and behavioral anomaly detection
According to the 2025 Verizon data breach investigation report, 74% of the breaches are related to humans, which are then related to insider misuse, credential abuse, social engineering, human error, etc. Therefore, it is recommended that enterprises invest in developing detection use cases in SOC to identify compromised insiders or manipulated employees by monitoring indicators such as suspicious access patterns, abnormal file downloads, privilege escalation attempts, unauthorized data transfers, and behavioral deviations.
4. AI-driven malware and autonomous attack detection
According to the 2025 state of ransomware survey from CrowdStrike, 76% of the enterprises struggle to match the speed of AI-powered attacks. Therefore, it is imperative to design detection use cases that account for adaptive malware, polymorphic threats, and autonomous attack behaviors, including PowerShell misuse, suspicious registry modifications, and anomalous process execution.
5. Misinformation and brand manipulation detection
According to Checkpoint research, brand impersonation dominates phishing attacks. The attack includes typosquatting domains, brand impersonation, credential-leak monitoring, and fake executive accounts. So, Agentic MDR should collaborate with threat intelligence sources spanning the dark web and deep web to build a proactive, context-aware defense posture that anticipates emerging threats, identifies attacker intent, and enables early detection and response.
6. Zero-trust access monitoring
A research firm, IBM, found that enterprises with mature zero-trust programs reduce breach costs by approximately 50%. Therefore, it is recommended to build SOC use cases that enforce continuous verification of every access attempt, at a minimum covering conditional access violations, risk-based authentication alerts, device compliance failures, and shadow IT detection.
7. Automated incident response with SOAR
As per Zensar’s internal findings, automated response and orchestration reduce the breach cost by approximately 20 to 40%. Attacks that used to take hours or days to respond are contained in minutes, thus reducing risk exposure and damage. The SOAR response includes automated SOAR use cases that evolve with threats, designed to minimize response time and contain risks. We have built many automated AI use cases, such as automatically deactivating compromised accounts, blocking identified malicious IP addresses, dynamically enforcing MFA based on risk signals, isolating infected or compromised endpoints, and initiating automated phishing investigations.
8. Threat hunting for AI-generated attacks
As per Zensar’s findings from real client environments, it has been observed that proactive threat hunting can reduce breach impact by approximately 30% – 40% and improve detection rates by up to 50%. A key aspect of threat hunting involves identifying anomalous language patterns, which can reveal AI-generated phishing emails or synthetic communications designed to bypass traditional filters. In parallel, threat hunters can detect coordinated low-volume or “low-and-slow” attacks, IOC and IOA, and correlate multi-stage attack patterns. These efforts are further strengthened by aligning detection strategies with the MITRE ATT&CK framework, ensuring a structured, adversary-centric view of tactics, techniques, and procedures.
The key challenge for enterprises today is not just adopting AI, but securely operationalizing it without expanding the attack surface. This requires a shift in mindset – from reactive security to a proactive, intelligence-driven, and AI-aware defense strategy.
Organizations that succeed will be those that:
Embed security into AI adoption from the outset
Leverage SOC as a centralized intelligence and response engine
Invest in automation, threat hunting, and continuous monitoring
Align detection and response with evolving AI-driven attack techniques
Ultimately, the equation is simple:
AI for security must go hand in hand with security for AI.
At Zensar, we believe the future lies in building an AI-secured enterprise, where innovation is enabled – but always underpinned by strong governance, real-time visibility, and adaptive defense mechanisms.
Because in a world of AI-powered attackers, only AI-aware, intelligence-driven defense will stay ahead.