Posted on: February 26th 2025

Q&A with Anand Kapoor, CIO of Straive 

Artificial intelligence is redefining cybersecurity—both as a defensive tool and a threat vector. As AI models grow more sophisticated, enterprises must rethink their security strategies, compliance frameworks, and risk management practices. 

In this conversation, Straive’s CIO shares insights on AI’s impact on cybersecurity, the evolving regulatory landscape, and the challenges of securing AI-driven enterprises.

Q1: DeepSeek is pushing AI into new territory. How do you see its impact on cybersecurity and risk management?

Anand Kapoor: DeepSeek represents a paradigm shift in AI’s ability to process vast datasets with greater context awareness and reasoning capabilities. In cybersecurity, this enables more sophisticated threat detection and autonomous response mechanisms. AI can now identify anomalies by pattern recognition and understanding intent, significantly enhancing proactive defense strategies.

However, these advancements come with risks. Cybercriminals can leverage similar AI capabilities to create highly evasive malware, automate large-scale attacks, and bypass traditional security measures. This fuels an AI-driven arms race, where defensive AI must continuously evolve to counter adversarial AI.

On the risk management front, DeepSeek-style models enhance predictive analytics for fraud detection, compliance enforcement, and insider threat monitoring. AI will soon interpret and adapt to evolving regulations in real-time, allowing enterprises to maintain compliance faster and more accurately. However, ensuring transparency, explainability, and bias mitigation will be critical as AI takes on more decision-making responsibilities.

Q2: AI-driven threat detection has evolved rapidly. What’s next?

Anand Kapoor: We are shifting from reactive security models to proactive, autonomous defense systems. AI is no longer just identifying known attack signatures; it can now detect behavioral deviations and predict threats before they materialize.

The next frontier is AI-driven security orchestration, where machine learning models will dynamically adjust defenses based on real-time threat intelligence. Thus, AI will continuously learn from new attack patterns and automatically update security protocols without human intervention. Zero-trust security architectures will become even more AI-integrated, ensuring continuous authentication and adaptive access control based on real-time risk assessments.

However, adversarial AI is evolving just as quickly. AI-generated phishing, deepfake-based impersonation, and automated cyberattacks will become increasingly difficult to detect with traditional methods. Enterprises must invest in self-learning AI countermeasures that continuously adapt rather than relying on static security policies. The key to the future of cybersecurity lies in AI’s ability to outthink and outmaneuver adversarial AI in real-time.

Q3: AI regulations are tightening worldwide. What’s the most significant compliance challenge for enterprises?

Anand Kapoor: The biggest challenge enterprises face isn’t just the complexity of AI regulations but also the ability to ensure continuous, real-time compliance at scale. Unlike traditional cybersecurity policies, AI regulations demand explainability, transparency, and fairness, requiring organizations to demonstrate how AI models make decisions—a significant shift in compliance expectations.

Harmonizing AI governance across multiple jurisdictions is a growing concern for global enterprises. Regulations such as the EU AI Act, NIST AI Risk Management Framework, and SEC cybersecurity mandates impose different, sometimes conflicting, requirements. Organizations must establish centralized AI compliance frameworks that adapt dynamically to evolving legal landscapes.

Another major challenge is third-party AI risk. Many enterprises rely on pre-trained AI models from external vendors, yet they remain accountable for ensuring regulatory compliance, ethical usage, and security of these systems. Enterprises risk legal exposure and reputational damage without full transparency into training data, bias mitigation, and decision-making logic. This is why explainable AI (XAI) is no longer just best practice—it’s essential for regulatory adherence and trust.

Q4: Generative AI is transforming industries. How can enterprises use it securely?

Anand Kapoor: Generative AI offers enormous potential but also introduces significant risks such as data leakage, the creation of misleading content, and adversarial manipulation. To mitigate these risks, enterprises should implement a multi-layered security strategy:

1. Strict Access Control – Limit which teams or users have access to generative AI systems and the sensitive data they process.
2. Automated Content Validation – All AI-generated outputs should be monitored for accuracy, security risks, and compliance violations before being used in operational settings.
3. Clear Usage Policies – Clearly define where and how generative AI can be used within the organization, particularly in sensitive areas like legal, HR, and financial reporting.
4. Continuous Auditing and Monitoring – Generative AI models evolve over time, and their outputs may drift or become vulnerable to new types of attacks. Regular auditing ensures ongoing alignment with security and compliance standards.

Security must be dynamic, and organizations should adopt a proactive approach to ensure that generative AI remains both an asset and a secure tool in the enterprise.

Q5: AI-powered attacks are becoming more sophisticated. How should enterprises respond?

Anand Kapoor: As cybercriminals leverage AI to launch highly targeted, automated, and adaptive attacks, enterprises must move beyond traditional defenses and adopt AI-driven security strategies. The key to staying ahead is autonomous, self-learning cybersecurity systems that detect, analyze, and neutralize threats in real time.

Key areas enterprises must focus on include:

• AI-Powered Phishing Detection – Advanced machine learning models that analyze linguistic patterns, contextual signals, and metadata to identify AI-generated phishing attempts.
• Automated Anomaly Detection – AI that contextually understands deviations from normal behavior rather than flagging isolated anomalies that may be false positives.
• Zero-Trust Enforcement – AI-driven authentication that continuously assesses risk based on user behavior, device fingerprinting, and session context, dynamically adapting access controls.
• AI Red Teaming – Security teams must actively train AI models to simulate cyberattacks and test defenses against adversarial AI techniques such as data poisoning and model evasion.

Cybersecurity is now an AI vs. AI battleground. Enterprises that fail to integrate AI into their defensive strategy will struggle to keep up with the rapidly evolving landscape of AI-powered threats.

Q6: If you could give cybersecurity leaders one key piece of advice, what would it be?

Anand Kapoor: Treat AI as a core pillar of cybersecurity, not just an add-on.

Many organizations still view AI as an incremental upgrade to existing security tools, but it represents a fundamental shift in cybersecurity. Those who embed AI into their security architecture from the ground up will be far better positioned than those who attempt to retrofit AI onto outdated frameworks.

Three critical areas to prioritize:

1. AI-First Security Architectures – Build adaptive, AI-native security models rather than trying to modify legacy systems to accommodate AI-driven threats.
2. Internal AI Expertise – Develop in-house AI capabilities so security teams can audit, refine, and defend AI-driven security tools rather than relying entirely on third-party solutions.
3. AI Transparency and Accountability – Ensure every AI-driven security decision is explainable, defensible, and adjustable in compliance with regulatory requirements.

Cybersecurity is now an AI-defined discipline. Organizations that fail to fully integrate AI-driven defenses will find themselves perpetually reactive—always one step behind attackers who already leverage AI as a weapon.

Final Thoughts

Anand Kapoor: AI is transforming cybersecurity at an unprecedented pace, redefining threat landscapes and defense strategies. From DeepSeek-style reasoning models to AI-generated cyber threats, enterprises are navigating a rapidly evolving battlefield where attackers and defenders leverage AI’s capabilities.

The key to success in this new era is proactive AI integration. Security leaders must shift from merely adopting AI tools to embedding AI-driven intelligence into every layer of their security infrastructure. This means:

• Moving beyond static defenses to adaptive, self-learning security models.
• Training AI against adversarial attacks through AI red teaming and continuous risk simulations.
• To meet global regulatory standards, ensuring transparency, fairness, and compliance in AI-driven decision-making.

The future belongs to organizations that treat AI as a strategic advantage and a battlefield. Attackers are already using AI to automate, disguise, and accelerate cyber threats—enterprises that fail to stay ahead of the curve will perpetually play defense. The time to build an AI-first cybersecurity strategy is now.

About the Author

Sudhakaran Jampala

Sudhakaran Jampala is a Content Writer (Marketing) with Straive, specializing in the cutting-edge technology areas of data science, machine learning, and AI. He is fascinated by the art of storytelling, which transforms data into sparkling insights by revealing patterns and infusing visual narratives.