“Security for AI” and “AI for Security”: The Dual Frontier of Cyber Defense

Artificial Intelligence (AI) is no longer just a tool – it’s the foundation of modern enterprise transformation. From revitalizing legacy IVR systems with conversational agents to deploying digital twins and enterprise-grade AI assistants, organizations are rapidly embedding AI across every business function. But as AI’s footprint grows, so too does its threat surface. When every new AI capability comes a new security challenge.

In this new digital reality, the relationship between “Security for AI” and “AI for Security” is not just complementary – it’s essential. Securing AI systems while leveraging AI to enhance security creates a symbiotic defense model that every modern organization must adopt.

Why “Security for AI” Has Become Critical

1. AI Expands the Attack Surface

The integration of AI-driven systems introduces novel vulnerabilities that traditional cybersecurity models were not built to address:

• Adversarial machine learning can manipulate model outputs.
• Data poisoning can compromise training datasets.
• Prompt injection and model manipulation in generative AI tools can cause information leaks or harmful outputs.
• Autonomous systems can act unpredictable or outside defined boundaries if not properly governed.

As AI becomes embedded in enterprise workflows, security teams must expand their threat models to include these new AI-specific risks.

2. Citizen Developers & No-Code/Low-Code Risks

The democratization of development through no-code and low-code platforms has enabled non-technical employees – “citizen developers” – to build and deploy AI-driven applications. While this unlocks unprecedented innovation and speed, it comes with serious risks:

• These creators often lack training in secure coding or data protection principles.
• Without guardrails, they may inadvertently expose sensitive data, violate compliance standards, or create unmonitored applications – what we now call “Shadow AI.”

3. Agentic AI Requires Dynamic Access Controls

AI agents are evolving. Today’s Agentic AI doesn’t just respond to commands – it initiates actions, collaborates with other agents, and learns dynamically. This demands a radical shift in how we manage identity and access:

• Traditional Identity and Access Management (IAM) systems rely on static roles and predefined permissions.
• Agentic AI needs real-time, context-aware, behavior-driven access control, with policies that adapt on the fly.

4. Lack of Visibility Creates Blind Spots

We can’t protect what we can’t see. In most enterprises, there’s little visibility into:

• How many no-code or AI-powered applications exist.
• Who built them, and how they interact with data and systems.
• What third-party models are being used, and whether they’ve been vetted.

Observability, AIOps, and MLOps are now essential – not just for operational efficiency, but for security assurance.

Why “AI for Security” is the Only Path Forward

Traditional security models, especially Security Operations Centers (SOCs), are falling behind. Manual alert triage, rule-based detection, and siloed tools can’t match the speed or scale of AI-enhanced threats.

1. AI-Driven Threats Demand AI-Powered Defense

Attackers are already using AI to automate phishing, generate deepfakes, evade detection, and find vulnerabilities at scale. Defensive teams must respond in kind:

• Behavioral analytics to detect anomalies.
• Automated threat hunting that learns from patterns and continuously improves.
• Real-time incident response triggered by intelligent rules, not static playbooks.

2. Event-Driven, Automated SOC is the Future

The next-gen SOC must be:

• Event-driven: Continuously ingesting and correlating data from AI systems, endpoints, and cloud services.
• Automated: Leveraging AI to take actions – quarantining endpoints, revoking access, or spinning up investigations – without waiting for human intervention.
• Intelligent: Using AI models to understand the intent and potential impact of anomalies, reducing false positives.

3. AIOps and MLOps as Security Enablers

• AIOps brings operational intelligence and automation to IT systems.
• MLOps ensures secure, traceable, and compliant deployment of machine learning models.

Together, they create the scaffolding for secure, monitored, and controlled AI ecosystems that scale.

Securing the Citizen Developer Without Slowing Innovation

To empower citizen developers without compromising enterprise security:

Embed Security by Design

No-code/low-code platforms must include:

• Pre-built security templates
• Data privacy defaults
• Role-based access to APIs and datasets

Automated Guardrails

Security should be invisible but omnipresent:

• Real-time risk detection while users build applications
• Auto-generated secure configurations
• Built-in runtime protection against common vulnerabilities

Just-in-Time Learning

Use AI-powered guidance, in the form of a “security coach”, to educate developers at the point of action – surfacing tips, warnings, and corrections as they build.

Rethinking IAM for Agentic AI

Agentic AI’s flexibility is its strength – and a challenge for traditional IAM. To adapt:

• Implement policy-based access systems that can adapt to changes in agent behavior, task, and purpose.
• Use AI to continuously profile agent activity and revoke privileges when anomalies arise.
• Ensure audibility and traceability so every agent’s action can be understood, traced, and governed.

Visibility and Observability: Foundations of AI Governance

A secure AI environment demands full-spectrum visibility:

AI Asset Management

Create an AI asset inventory that tracks:

• Models in use
• No-code/low-code applications
• Associated data sources
• Ownership and usage history

Observability Pipelines

Use logging, telemetry, and metrics to monitor:

• AI agent activity
• Model drift or anomalies
• Data usage and flow patterns

Lifecycle Security Reviews

Implement automated security gates at every stage of development and deployment – ensuring compliance, integrity, and accountability.

“Security for AI” and “AI for Security”: Two Sides of the Same Coin

Securing AI and leveraging AI for security are no longer optional – they are interdependent pillars of digital resilience.

Key Principles for the New AI Security Paradigm:

• Automate security at every layer – from identity to detection to response.
• Make IAM dynamic – tailored for agents, not just humans.
• Illuminate all corners of our AI landscape – no-code, low-code, and Shadow AI included.
• Outsmart adversaries with defensive AI – stay one step ahead through continuous learning.
• Empower users – especially citizen developers – with safe, intuitive tools to build securely.

Final Thoughts

As organizations push the boundaries of AI innovation, they must push just as hard on AI security. The balance between empowerment and protection, between creativity and control, is delicate – but it’s not optional.

The winners in this new era will be those who build with AI and defend with AI – simultaneously.