Amex CISO fights threats at machine velocity With AI

Balancing the paradox of defending one of many world’s main journey, software program and providers companies towards the accelerating threats of AI illustrates why CISOs have to be steps forward of the newest adversarial AI tradecraft and assault methods.    

As a number one world B2B journey platform, American Categorical International Enterprise Journey (Amex GBT) and its safety staff are doing simply that, proactively confronting this problem with a twin concentrate on cybersecurity innovation and governance. With deep roots in a financial institution holding firm, Amex GBT upholds the very best knowledge privateness requirements, safety compliance and threat administration. This makes safe, scalable AI adoption a mission-critical precedence.

Amex GBT Chief Info Safety Officer David Levin is main this effort. He’s constructing a cross-functional AI governance framework, embedding safety into each section of AI deployment and managing the rise of shadow AI with out stifling innovation. His method gives a blueprint for organizations navigating the high-stakes intersection of AI development and cyber protection.

The next are excerpts from Levin’s interview with VentureBeat:

VentureBeat: How is Amex GBT utilizing AI to modernize risk detection and SOC operations?

David Levin: We’re integrating AI throughout our risk detection and response workflows. On the detection aspect, we use machine studying (ML) fashions in our SIEM and EDR instruments to identify malicious habits quicker and with fewer false positives. That alone accelerates how we examine alerts. Within the SOC, AI-powered automation enriches alerts with contextual knowledge the second they seem. Analysts open a ticket and already see crucial particulars; there’s now not a must pivot between a number of instruments for fundamental info.

AI additionally helps prioritize which alerts are possible pressing. Our analysts then spend their time on the highest-risk points quite than sifting by noise. It’s an enormous increase in effectivity. We will reply at machine velocity the place it is smart, and let our expert safety engineers concentrate on complicated incidents. Finally, AI helps us detect threats extra precisely and reply quicker.

VentureBeat: You additionally work with managed safety companions like CrowdStrike OverWatch. How does AI function a pressure multiplier for each in-house and exterior SOC groups?
Levin: AI amplifies our capabilities in two methods. First, CrowdStrike OverWatch offers us 24/7 risk looking augmented by superior machine studying. They continually scan the environment for refined indicators of an assault, together with issues we would miss if we relied on handbook inspection alone. Which means we’ve a top-tier risk intelligence staff on name, utilizing AI to filter out low-risk occasions and spotlight actual threats.

Second, AI boosts the effectivity of our inside SOC analysts. We used to manually triage much more alerts. Now, an AI engine handles that preliminary filtering. It could shortly distinguish suspicious from benign, so analysts solely see the occasions that want human judgment. It appears like including a sensible digital teammate. Our workers can deal with extra incidents, concentrate on risk looking, and choose up superior investigations. That synergy—human experience plus AI assist—drives higher outcomes than both alone

VentureBeat: You’re heading up an AI governance framework at GBT, based mostly on NIST rules. What does that appear to be, and the way do you implement it cross-functionally?

Levin: We leaned on the NIST AI Threat Administration Framework, which helps us systematically assess and mitigate AI-related dangers round safety, privateness, bias and extra. We fashioned a cross-functional governance committee with representatives from safety, authorized, privateness, compliance, HR and IT. That staff coordinates AI insurance policies and ensures new initiatives meet our requirements earlier than going dwell.

Our framework covers the complete AI lifecycle. Early on, every use case is mapped towards potential dangers—like mannequin drift or knowledge publicity—and we outline controls to deal with them. We measure efficiency by testing and adversarial simulations to make sure the AI isn’t simply fooled. We additionally insist on not less than some stage of explainability. If an AI flags an incident, we need to know why. Then, as soon as methods are in manufacturing, we monitor them to verify they nonetheless meet our safety and compliance necessities. By integrating these steps into our broader threat program, AI turns into a part of our general governance quite than an afterthought.

VentureBeat: How do you deal with shadow AI and guarantee workers observe these insurance policies?

Levin: Shadow AI emerged the second public generative AI instruments took off. Our method begins with clear insurance policies: Staff should not feed confidential or delicate knowledge into exterior AI providers with out approval. We define acceptable use, potential dangers, and the method for vetting new instruments.

On the technical aspect, we block unapproved AI platforms at our community edge and use knowledge loss prevention (DLP) instruments to stop delicate content material from being uploaded. If somebody tries utilizing an unauthorized AI web site, they get alerted and directed to an accepted different. We additionally rely closely on coaching. We share real-world cautionary tales—like feeding a proprietary doc right into a random chatbot. That tends to stay with folks. By combining person schooling, coverage readability and automatic checks, we will curb most rogue AI utilization whereas nonetheless encouraging official innovation.

VentureBeat: In deploying AI for safety, what technical challenges do you encounter, for instance, knowledge safety, mannequin drift, or adversarial testing?

Levin: Information safety is a main concern. Our AI typically wants system logs and person knowledge to identify threats, so we encrypt these feeds and limit who can entry them. We additionally be sure that no private or delicate info is used except it’s strictly vital.

Mannequin drift is one other problem. Assault patterns evolve continually. If we depend on a mannequin skilled on final 12 months’s knowledge, we threat lacking new threats. We’ve got a schedule to retrain fashions when detection charges drop or false positives spike.

We additionally do adversarial testing, basically red-teaming the AI to see if attackers might trick or bypass it. That may imply feeding the mannequin artificial knowledge that masks actual intrusions, or attempting to govern logs. If we discover a vulnerability, we retrain the mannequin or add further checks. We’re additionally huge on explainability: if AI recommends isolating a machine, we need to know which habits triggered that call. That transparency fosters belief within the AI’s output and helps analysts validate it.

VentureBeat: Is AI altering the function of the CISO, making you extra of a strategic enterprise enabler than purely a compliance gatekeeper?

Levin: Completely. AI is a major instance of how safety leaders can information innovation quite than block it. As a substitute of simply saying, “No, that’s too dangerous,” we’re shaping how we undertake AI from the bottom up by defining acceptable use, coaching knowledge requirements, and monitoring for abuse. As CISO, I’m working carefully with executives and product groups so we will deploy AI options that really profit the enterprise, whether or not by bettering the client expertise or detecting fraud quicker, whereas nonetheless assembly laws and defending knowledge.

We even have a seat on the desk for giant choices. If a division needs to roll out a brand new AI chatbot for journey reserving, they contain safety early to deal with threat and compliance. So we’re shifting past the compliance gatekeeper picture, getting into a task that drives accountable innovation.

VentureBeat: How is AI adoption structured globally throughout GBT, and the way do you embed safety into that course of?

Levin: We took a worldwide heart of excellence method. There’s a core AI technique staff that units overarching requirements and tips, then regional leads drive initiatives tailor-made to their markets. As a result of we function worldwide, we coordinate on greatest practices: if the Europe staff develops a strong course of for AI knowledge masking to adjust to GDPR, we share that with the U.S. or Asia groups.

Safety is embedded from day one by “safe by design.” Any AI mission, wherever it’s initiated, faces the identical threat assessments and compliance checks earlier than launch. We do risk modeling to see how the AI might fail or be misused. We implement the identical encryption and entry controls globally, but in addition adapt to native privateness guidelines. This ensures that irrespective of the place an AI system is constructed, it meets constant safety and belief requirements.

VentureBeat: You’ve been piloting instruments like CrowdStrike’s Charlotte AI for alert triage. How are AI co-pilots serving to with incident response and analyst coaching?

Levin: With Charlotte AI we’re offloading a whole lot of alert triage. The system immediately analyzes new detections, estimates severity and suggests subsequent steps. That alone saves our tier-1 analysts hours each week. They open a ticket and see a concise abstract as a substitute of uncooked logs.

We will additionally work together with Charlotte, asking follow-up questions, together with, “Is that this IP tackle linked to prior threats?” This “conversational AI” facet is a serious assist to junior analysts, who study from the AI’s reasoning. It’s not a black field; it shares context on why it’s flagging one thing as malicious. The online result’s quicker incident response and a built-in mentorship layer for our staff. We do keep human oversight, particularly for high-impact actions, however these co-pilots allow us to reply at machine velocity whereas preserving analyst judgment.

VentureBeat: What do advances in AI imply for cybersecurity distributors and managed safety service suppliers (MSSPs)?

Levin: AI is elevating the bar for safety options. We anticipate MDR suppliers to automate extra of their front-end triage so human analysts can concentrate on the hardest issues. If a vendor can’t present significant AI-driven detection or real-time response, they’ll wrestle to face out. Many are embedding AI assistants like Charlotte straight into their platforms, accelerating how shortly they spot and comprise threats.

That mentioned, AI’s ubiquity additionally means we have to see previous the buzzwords. We check and validate a vendor’s AI claims—“Present us how your mannequin realized from our knowledge,” or “Show it could deal with these superior threats.” The arms race between attackers and defenders will solely intensify, and safety distributors that grasp AI will thrive. I totally anticipate new providers—like AI-based coverage enforcement or deeper forensics—rising from this development.

VentureBeat: Lastly, what recommendation would you give CISOs beginning their AI journey, balancing compliance wants with enterprise innovation?

Levin: First, construct a governance framework early, with clear insurance policies and threat evaluation standards. AI is simply too highly effective to deploy haphazardly. If you happen to outline what accountable AI is in your group from the outset, you’ll keep away from chasing compliance retroactively.

Second, companion with authorized and compliance groups upfront. AI can cross boundaries in knowledge privateness, mental property, and extra. Having them onboard early prevents nasty surprises later.

Third, begin small however present ROI. Decide a high-volume safety ache level (like alert triage) the place AI can shine. That fast win builds credibility and confidence to develop AI efforts. In the meantime, spend money on knowledge hygiene—clear knowledge is all the things to AI efficiency.

Fourth, practice your folks. Present analysts how AI helps them, quite than replaces them. Clarify the way it works, the place it’s dependable and the place human oversight remains to be required. A well-informed workers is extra prone to embrace these instruments.

Lastly, embrace a continuous-improvement mindset. Threats evolve; so should your AI. Retrain fashions, run adversarial assessments, collect suggestions from analysts. The know-how is dynamic, and also you’ll must adapt. If you happen to do all this—clear governance, robust partnerships, ongoing measurement—AI could be an infinite enabler for safety, letting you progress quicker and extra confidently in a risk panorama that grows by the day.

VentureBeat: The place do you see AI in cybersecurity going over the subsequent few years, each for GBT and the broader {industry}?

Levin: We’re heading towards autonomous SOC workflows, the place AI handles extra of the alert triage and preliminary response. People oversee complicated incidents, however routine duties get totally automated. We’ll additionally see predictive safety—AI fashions that forecast which methods are most in danger, so groups can patch or phase them prematurely.

On a broader scale, CISOs will oversee digital belief, making certain AI is clear, compliant with rising legal guidelines and never simply manipulated. Distributors will refine AI to deal with all the things from superior forensics to coverage tuning. Attackers, in the meantime, will weaponize AI to craft stealthier phishing campaigns or develop polymorphic malware. That arms race makes strong governance and steady enchancment crucial.

At GBT, I anticipate AI to permeate past the SOC into areas like fraud prevention in journey bookings, person habits analytics and even personalised safety coaching. Finally, safety leaders who leverage AI thoughtfully will acquire a aggressive edge—defending their enterprises at scale whereas liberating expertise to concentrate on probably the most complicated challenges. It’s a serious paradigm shift, however one which guarantees stronger defenses and quicker innovation if we handle it responsibly.