As enterprises race to deploy AI-driven systems at unprecedented speed and scale, security has become one of the most consequential design constraints shaping modern infrastructure. The challenge is to enable rapid experimentation, autonomous decision-making, and distributed cloud architectures without quietly accumulating risk.
Kaushik “KJ” Jangiti has spent over a decade working inside that tension. A cybersecurity expert and AI security researcher, KJ has architected enterprise security programs across AI platforms, data cloud environments, SaaS, IoT, retail, and financial services, advising organizations on how to scale without losing control.
In this conversation with Alltech Magazine, he shares a deeply practical perspective on how security leaders must rethink controls, automation, insider risk, and resilience in an era where AI systems act faster than humans can supervise them. Drawing on real-world frameworks he has developed and applied at scale, KJ explains how security can shift from a blocking function into a growth enabler, and why the organizations that get this right early will define the next generation of trusted, AI-powered enterprises.
You design security infrastructure for AI and data-driven enterprises where speed and scale are non-negotiable. How do you decide which security controls must be immovable and where teams can safely innovate without friction?
The way I think about this comes down to separating what’s sacred from what’s flexible. Some controls are foundational identity governance, encryption, data classification, audit trails, and regulatory compliance. You don’t negotiate on those. But here’s the thing: if engineers feel those controls as friction, you’ve already lost. I design them to be invisible. Baked into platforms, embedded in pipelines, enforced by default. People shouldn’t have to think about them. They just work.
When those foundations are solid, I focus everything else on speed. I’m a big believer in the Pareto Principle here. In my experience, about 20% of your security controls mitigate 80% of your actual enterprise risk. The real skill is figuring out which 20% that is and making those controls airtight and automatic. Everything else? That’s where I create room to move. Sandboxed environments. Pre-approved templates. Automated guardrails that let teams experiment without waiting on approvals.
I’ve learned that security teams who default to “no” end up creating shadow IT. People route around you. But if you engineer the secure path also to be the easy path, suddenly you’re not fighting human nature, you’re working with it.
This philosophy has shaped the frameworks I’ve developed throughout my career, enabling AI-native data platforms and cloud environments at enterprise scale. Shifting controls left into the development pipeline. Giving developers immediate feedback instead of blocking them at deployment. Using adoption metrics to measure real maturity rather than just compliance checkboxes. When security feels effortless, it actually gets adopted.
You have worked across AI, data cloud, SaaS, IoT, retail, and financial services. What patterns do you consistently see in organizations that successfully scale cloud native infrastructure without creating new security blind spots?
Early in my career, I started noticing something that’s held true across every environment I’ve secured: AI platforms, retail systems, IoT infrastructure, and financial services.
The organizations that scale cloud-native well don’t win by buying better tools. They win because they institutionalize repeatable operating patterns.
I’ve spent years codifying what separates the ones that succeed from the ones that end up with sprawling, ungovernable environments.
The first pattern is deceptively simple: relentless clarity on assets and identity. The mature organizations know exactly what they’re running, where sensitive data lives, which APIs are exposed, and which human and machine identities can access what.
Most cloud security blind spots are the predictable consequence of unmanaged inventory, inconsistent tagging, orphaned resources, and service accounts that silently accumulate privilege. What I’ve pushed organizations toward is treating identity and asset governance as foundational platform capabilities, not afterthought compliance exercises. When you get that right, half your security problems never materialize in the first place.
The second pattern ties back to the Pareto discipline I mentioned earlier. Instead of the instinct to “secure everything equally,” you focus resources on controls that eliminate the majority of real-world risk, then codify those into policy-as-code and infrastructure-as-code so security isn’t dependent on tribal knowledge. I’ve seen organizations transform their risk posture in months, not years, by applying this kind of focused discipline.
The cultural layer matters just as much. In the strongest security organizations I’ve helped build, security is an engineering outcome, not a gate. Platform teams create paved roads. Security champions embed inside product teams.
Risk decisions get made with real context: likelihood, impact, operational cost. That’s how you scale fast without quietly drifting into chaos, and it’s a model I’ve refined across a decade of building security programs from the ground up.
As enterprises race to deploy AI-powered systems, how do you secure data pipelines and models while still enabling experimentation and rapid iteration for engineering teams?
The mistake I see repeated across organizations rushing into AI is treating security as “model security” alone. But having built security architectures for AI-native platforms over the past several years, I’ve learned the risk concentrates in the pipeline on what data gets ingested, how it’s transformed, what’s logged, what’s retrieved at inference time, and critically, what an agent is actually allowed to do with external tools.
So I secure the full lifecycle: data classification and lineage at ingestion, access governance at every hop, integrity checks to reduce poisoning risk, and auditability that answers the hard questions: what did the model see, what did it retrieve, and what action did it take?
This is why I’ve become a strong advocate for an AI Bill of Materials thinking in enterprise security. Just like software supply chain security demands knowing your dependencies, AI security demands knowing your data lineage, model provenance, prompt templates, and tool permissions.
I’m actively working to operationalize this approach and push its adoption through industry publications and conference presentations because most organizations haven’t yet connected the dots between traditional SBOM practices and what AI-native systems actually require.
The organizations that adopt this thinking early can trace any AI output back to its inputs, which becomes essential for both security response and regulatory accountability.
To keep iteration fast, I apply the same governed-speed philosophy I described earlier. The goal hasn’t changed across any environment I’ve secured: teams move at product speed, but the system gives you traceability, control, and surgical response capability when something shifts whether that’s training data drift, prompt injection attempts, or tool misuse.
You are known for your work in AI-powered threat detection and response. How does automation change the way security teams should think about risk prioritization and decision-making at the enterprise level?
Automation changes the job from chasing alerts to making higher-quality decisions at speed. At enterprise scale, volume alone will drown you, so “alert on everything” becomes its own failure mode. What I’ve operationalized instead is context-led prioritization—because context is what any AI system needs, and what humans need too, to make the right call. That means weighting signals by exploitability, blast radius, asset criticality, and how quickly something can spread through identity, data, and interconnected services, not just a severity label.
It also changes how the response should work. For low-regret scenarios, automation should take immediate containment actions, revoking risky tokens, isolating suspicious workloads,and enforcing step-up authentication while humans focus on what machines still can’t do well: ambiguous investigations, tradeoffs, and systemic fixes.
In practice, I apply the same Pareto discipline here: get the small set of detections and automated playbooks that prevent most real incidents from working reliably, measure outcomes, and continuously tune. The goal isn’t “more automation,” it’s faster, more defensible risk decisions that scale.
Insider risk remains one of the most complex challenges for security leaders. How do you design governance and monitoring frameworks that protect the organization without eroding trust or slowing collaboration?
Insider risk is where heavy-handed security backfires fastest. If your default move is to block most things and alert on everything, you’ll create the distrust you’re trying to prevent and people will route around the system.
The way I approach it is the same “sacred vs. flexible” separation I use everywhere else. What’s sacred is clarity and fairness: transparent policies, role-based access, separation of duties, and audit trails that are strong enough to support accountability without turning the workplace into a surveillance culture. Trust doesn’t erode because controls exist; it erodes when controls feel personal, inconsistent, or punitive.
From there, I design monitoring around behavioral signals, not blanket observation. You’re looking for patterns that correlate with real risk privilege spikes that don’t match a role, unusual data movement, access outside normal baselines, or sudden shifts in tool usage.
I apply DARE a framework I developed as an operating loop here: Detect what’s running and what’s changing, Assess anomalies with context rather than blind flagging, Redirect users toward safer paths through guardrails and secure defaults, and invest continuously in Education and Evaluation. This is where the security champions model matters: champions help teams understand these controls protect them too from compromised accounts, unclear boundaries, and accidental exposure. The outcome I’ve consistently aimed for is simple: collaboration by default, with rapid detection when behavior genuinely drifts into risk without treating every employee like a suspect.
Looking ahead, as infrastructure becomes more distributed and AI becomes more autonomous, what mindset shift do you believe enterprise leaders must adopt to ensure security continues to enable growth rather than constrain it?
The mindset shift is fundamental: moving from security-as-prevention to security-as-resilience. Distributed systems and autonomous AI agents won’t be perfectly predictable, and chasing perfect prevention quickly turns into paralysis. The posture that works is to build systems that are observable, governable, and recoverable, so that when something goes wrong, you can contain it, understand it, and fix it without freezing innovation. Prevention still matters, but resilience is what lets organizations operate at scale with confidence.
That shift also changes how security ownership scales. When agents can make thousands of decisions humans will never individually review, central teams can’t keep up by approving everything. They win by building paved roads, secure defaults, guardrails, and runtime checks that make safe behavior the easiest behavior.
This is why I built frameworks like DARE as continuous loops that detect what’s running, surface real anomalies, and drive correction and education without slowing delivery.
And when leaders tie security to business outcomes, customer trust, uptime, regulatory access, and market confidence, it stops being a cost center and becomes a growth capability. The organizations that design for resilience early deploy autonomy responsibly; those that bolt it on later pay for it in incidents, reputational damage, and lost momentum.
