intuit

You can’t audit how AI thinks, but you can audit what it does In this Help Net Security interview, Wade Bicknell, Head, IT Security & Operations, CFA Institute, discusses how CISOs can use AI while maintaining security and governance. He explains why AI presents both defensive opportunities and emerging risks, and how leadership must balance innovation, control, and accountability in cybersecurity. How should a CISO be thinking about using, or guarding against, AI/ML systems internally (for fraud detection, threat hunting)? It’s a dual-sided challenge. Many emerging companies are focused on integrating AI into their defensive capabilities, from fraud detection to threat hunting, but CISOs must also recognize that adversaries are doing the same. We’re still in the early days of AI being used offensively, but that stage is approaching quickly. AI is a force multiplier, it can accelerate defense, but it can also amplify malicious creativity when paired with human intent. This means establishing internal boundaries for experimentation, protecting the data fed into AI models, and creating early governance frameworks around use. In short, we must learn to defend with AI while simultaneously defending against it. How can an organization ensure AI tools are auditable, explainable, and robust against adversarial attacks? Traditional audit and oversight models assume linear, explainable logic, but AI doesn’t think in straight lines. It’s like assigning a complex task to an autonomous intern who decides how to get it done. You see the outcome, but not always the process. That’s the core challenge, we often can’t unpack how an AI reached a decision. The best we can do is validate that its outputs align with our intent and remain within our ethical and operational boundaries. Organizations should combine technical transparency (model documentation, data lineage) with operational oversight, human review boards, AI red-teaming, and continuous testing for drift or adversarial manipulation. We may never be able to audit AI’s thought process, but we can, and must, continuously audit its outcomes and impact. What standards or controls should be in place when AI is used in investment operations or member services? Any organization allowing AI to make investment or customer-facing decisions without human oversight is accepting significant risk. AI lacks moral and contextual awareness, it doesn’t intuit “don’t harm,” “don’t mislead,” or “act fairly.” In my own experience using AI for coding and analytics, I’ve seen how quickly it can “forget” prior guardrails and revert to earlier behavior. That’s why in financial or member-service contexts, AI must operate under strict governance that includes: Human-in-the-loop decisioning for all high-impact actions Defined ethical boundaries for what AI can decide or recommend Bias and performance testing at regular intervals Accountability for AI outputs, including override mechanisms We can’t assume AI understands the intent behind our requests, we must encode that intent and continuously verify it’s being upheld. How should firms prepare for audits of AI systems where transparency might conflict with intellectual property constraints or model complexity? This is one of the most overlooked risk areas. Many organizations don’t understand what their employees are feeding into AI systems with sensitive data, code, or proprietary logic can leak easily through everyday use of generative tools. Until a major event forces the issue, many will underestimate the exposure. The best preparation is proactive: Data classification policies defining what may or may not enter AI systems Internal model registries to track usage, inputs, ownership, and updates Third-party attestations or secure audit mechanisms that protect IP while allowing sufficient transparency Employee education on the risks of AI data misuse or leakage By doing these things, and documenting that you do them, you achieve the true intent of an audit: proving that controls are in place. When governance, transparency, and accountability are embedded in operations, you don’t just prepare for audit, you make your organization audit-proof by design. In anti-money laundering or fraud detection, how do you ensure explanations don’t reveal sensitive data while remaining actionable? In AML and fraud detection, timing is everything. Explanations are only useful if they help prevent a transaction before it occurs. AI will supercharge both payments and fraud, so prevention must shift to the front of the process. At the same time, model explainability must balance regulatory and privacy demands. The most effective approach is layered disclosure, provide investigators with enough insight (patterns, behavioral anomalies, clusters) to act, but without revealing unnecessary personal or transactional data. The goal is actionable transparency, giving teams the information they need to act decisively and ethically, without compromising privacy or regulatory integrity.

Threat actors are leveraging a Unicode character to make phishing links appear like legitimate Booking.com links in a new campaign distributing malware. The attack makes use of the Japanese hiragana character, ん, which can, on some systems, appear as a forward slash and make a phishing URL appear realistic to a person at a casual glance. BleepingComputer has further come across an Intuit phishing campaign using a lookalike domain using the letter L instead of 'i' in Intuit. Booking.com phishing links using Japanese homoglyph The attack, first spotted by security researcher JAMESWT, abuses the Japanese hiragana character “ん” (Unicode U+3093), which closely resembles the Latin letter sequence '/n' or '/~', at a quick glance in some fonts. This visual similarity enables scammers to create URLs that appear to belong to the genuine Booking.com domain, but direct users to a malicious site. Below is a copy of the phishing email shared by the security researcher: The text in the email, https://admin.booking.com/hotel/hoteladmin/... itself is deceptive. While it may look like a Booking.com address, the hyperlink points to: https://account.booking.comんdetailんrestric-access.www-account-booking.com/en/ When rendered in a web browser's address bar, the 'ん' characters can trick users into thinking they are navigating through a subdirectory of booking.com. In reality, the actual registered domain is www-account-booking[.]com, a malicious lookalike, and everything before that is just a deceptive subdomain string. Victims who click through are eventually redirected to: www-account-booking[.]com/c.php?a=0 This in turn delivers a malicious MSI installer from a CDN link, https://updatessoftware.b-cdn[.]net/john/pr/04.08/IYTDTGTF.msi Samples of the malicious site are available on abuse.ch's MalwareBazaar, with any.run analysis showing the infection chain. The MSI file is used to drop further payloads, potentially including infostealers or remote access trojans. This phishing tactic exploits homoglyphs. A homoglyph is a character that looks similar to another character but belongs to a different character set or alphabet. These visually similar characters can be exploited in phishing attacks or to create misleading content. For example, Cyrillic character "О" (U+041E) may appear identical to the Latin letter "O" (U+004F) to a human, but they are different characters. Given their visual similarities, homoglyphs have been leveraged time and time again by threat actors in homograph attacks and phishing emails. Defenders and software developers have also, over the last few years, rolled out security measures that make it easy for users to distinguish between distinct homoglyphs. This isn't the first time threat actors have targeted Booking.com customers either. In March this year, Microsoft warned of phishing campaigns impersonating Booking.com and using ClickFix social engineering attacks to infect hospitality workers with malware. In 2023, Akamai revealed how hackers were redirecting hotel guests to fake Booking.com sites to steal credit card information. 'Lntuit' not Intuit BleepingComputer's Sergiu Gatlan spotted a separate phishing campaign involving users being targeted with Intuit-themed emails. These emails appear to come from and take you to intuit.com addresses, but instead use domains starting with Lntuit—which, in lowercase, can resemble "intuit" in certain fonts. A simple yet effective technique. The unusually narrow layout of this email in desktop clients suggests it was primarily designed for mobile viewing, with attackers banking on mobile users clicking the "Verify my email" phishing link without closely inspecting it. The button takes users to: https://intfdsl[.]us/sa5h17/ Interestingly, the illicit link, when accessed directly i.e. not from the target user's email account appears to redirect the user back to the legitimate Intuit.com login page at https://accounts.intuit.com/app/sign-in. These incidents are a reminder that attackers will continue to find creative ways to abuse typography for social engineering. To protect yourself, always hover over links before clicking to reveal the true target. Users should always check the actual domain at the rightmost end of the address before the first single / — this is the real registered domain. Granted, the use of visually deceptive Unicode characters like 'ん' create additional hurdles, and demonstrates that visual URL inspection alone isn't foolproof. Keeping endpoint security software up to date adds another layer of defense against attacks since modern phishing kits often deliver malware directly, after a phishing link is clicked. Overall prevention scores can hide what happens after initial access. Once attackers are using valid credentials, prevention drops sharply. The Blue Report 2026 measures defenses technique by technique across 338 million simulations run in customer production environments. Get the report