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When it comes to email security software, the cybersecurity industry has become very good at one thing: scoring the message that's already in the inbox. We've gone from blocklists to signature matching to behavioral ML, and each generation of email security innovation was a genuine improvement over the last.  But the losses keep climbing anyway. The median time it takes someone to click a phishing link is just 21 seconds after opening the email, and another 28 seconds to hand over credentials or payment data. That's under a minute start to finish. That number hasn't moved much in years despite everything we've thrown at the problem. It's time for a different approach. But first, we need to understand modern challenges. We're filtering messages, but attackers are running campaigns

Somewhere right now, malware running on a compromised machine is checking in with an AI model, asking it for a new version of itself. Google's Threat Intelligence Group caught this happening in late 2025. The malware, nicknamed PROMPTFLUX, does this every hour it runs, and each version comes back looking different from the last. By the time a security tool learns to recognize it, it has already changed shape again. This isn't a rare glitch or a lab experiment. It's a preview of how a growing share of attacks work today and why legacy SIEM platforms built to detect known patterns are starting to fall behind. Legacy security tools were built to detect, not to adapt A SIEM is a system that collects logs from every part of a company's network and looks for signs of an attack. For years, it worked like a security guard with a very long memory. The guard learns what a break-in looks like - a certain kind of file, a pattern of behavior, a code signature, and watches fo...

We had an enterprise customer tell us their entire AI footprint was Copilot. That was the whole answer. One tool, one line item, done. We ran the first scan. Copilot wasn't even close to number one. Claude was. OpenAI came in second. Copilot was third. Nobody on the security team knew because Claude doesn't show up the way a SaaS app used to show up. There's no single login screen, no single admin console, no one place to look. That's the part most security teams miss. Claude isn't one surface. It's six. The six places Claude actually runs Claude Enterprise and Connected Apps. This is the surface everyone pictures: employees typing into Claude, OAuth'd into Google Drive, GitHub, Slack, and Jira, asking Claude to act on what's inside. The audit log shows that a connection happened. It does not show what got pulled into the prompt or what came back out. A finance analyst can drop a quarter of board materials into a conversation in ten second...

Security is fundamentally about knowing what a system is supposed to do, then catching it when it does something else. For software with deterministic execution paths, that is a tractable problem. For AI agents, it is not. An agent does not follow a fixed code path. It reasons toward a goal, selects tools based on that reasoning, and adapts its next action based on what those tools return. The same input can produce a different sequence of actions depending on context, session history, and what an external tool happened to return. Behavior is the attack surface, and behavior changes. Traditional proxies and AI firewalls were built to inspect content: what a user sent, what a model returned. Intent security asks a different question: is this agent doing what it was built to do, in this context, for this user, right now? Answering that requires building a behavioral baseline for every agent and measuring deviation from it continuously. That is the problem Lasso was built to solve....

For a long time, the cybersecurity industry has framed social engineering as a psychological problem. We treat it as a battle of wits between a charismatic con artist and an unsuspecting employee. The prevailing wisdom says that if we just train our people to better identify scams or tear down malicious infrastructure slightly faster, we can stay ahead of scammers. But looking at the threat landscape from a purely threat-intelligence perspective tells us something entirely different: Modern social engineering is more than a psychological game. It's a highly optimized, industrialized deception economy. Attackers run campaigns like hyper-efficient businesses. They have Customer Acquisition Costs, operational budgets, and strict Return on Investment (ROI) targets. This is the part the industry doesn't like to sit with: If we want to truly break the social engineering attack chain , we have to stop focusing exclusively on building higher walls or executing reactive takedowns...

Enterprises are deploying AI agents faster than their identity security programs can keep pace with. Every agent that enters production carries inherited permissions, operates outside traditional IAM visibility, and makes consequential decisions without human sign-off at each step. What is agentic AI in identity security, who owns the risk it generates, and what controls actually work at scale: that's what this guide covers. Why AI Agent Risk Doesn't Behave Like Traditional Security Risk Most enterprise security models rest on a foundational assumption: a human initiates access, a policy evaluates that request, and a control either permits or denies it. AI agents break every link in that chain simultaneously. Understanding what agentic AI in identity security is requires moving beyond the surface-level observation that agents are "automated." Automation has existed in enterprise environments for decades. What makes agentic AI categorically different is autonom...

When I work on an incident, the first question I ask is almost never "what malware ran." It's "whose credentials did it use, and what was that account allowed to touch." Nine times out of ten, the interesting part of the story isn't the exploit. It's the access. The exploit gets you in the door. The privilege is what lets you walk through the building. For thirty years, that question had a human-shaped answer. A person clicked something, a person got phished, a person reused a password, an admin left a service account sitting on a domain controller with a password from 2014. The identity at the center of the incident belonged to somebody with a badge and a manager. That assumption is quietly dying, and most enterprises have not adjusted their controls to match. The actor on your network is increasingly not a person at all. It is a workload, a script, a bot, an API (application programming interface) client, and now an AI agent that can reason, plan...

Attackers prefer the path of least resistance. Why break in when you can log in? That is what makes working credentials so valuable. A leaked password, token, or API key does more than reveal sensitive data; it offers a way in. No vulnerability or privilege escalation chain necessary. If the credential is valid, the attacker can simply use it, and the session will look like ordinary activity. This shifts the questions from "where can attackers break in?" to "where do usable keys tend to accumulate? Increasingly, the answer is the developer laptop: one of the most instrumented machines most companies own, and one of the easiest places for credentials to go unnoticed. The endpoint is watched for malware, behavior, posture, and configuration. But a valid plaintext credential is something else: a door an attacker may not need to force. A fully patched fleet can still have usable keys scattered across its devices, enough to turn one foothold into something much larger....

Why Authentication Context Matters Most people think logging in is a small act. Type your password, type your code, tap a screen, scan a face, and move on. But to the systems on the other side, the method behind that moment can matter just as much as the fact that it happened at all. That is where two strangely named but surprisingly important identity concepts enter the story: OIDC's AMR and SAML 2.0's AuthnContext. They sound like the kind of acronyms that only standards committees could love, yet both were created to answer a deeply human question in digital form: How sure a system has to be before it trusts someone? The backstory starts with the internet growing up. Early online services often treated authentication as a light switch: either the user was in, or the user was out. But as online systems began handling payroll, health records, taxes, academic data, contracts, and financial approvals, that simple model started to crack. A login backed by a reused password is not ...

Cybersecurity is entering a new phase. It's one where the gap between awareness and operational execution is becoming the industry's biggest challenge. That's what stood out to me most after reviewing the results of the 2026 Bitdefender Cybersecurity Assessment , which found that organizations have never had greater insight into the risks they face, yet turning that understanding into meaningful action remains a persistent challenge. Nowhere is that gap more visible, in my view, than in how organizations handle breach transparency. We surveyed 1,200 IT and cybersecurity professionals across six countries: France, Germany, Italy, Singapore, the United Kingdom, and the United States. Respondents ranged from frontline employees to IT managers to CISOs, all working within organizations with 500 or more employees. A Governance Problem, Not an Attacker Problem One of the most troubling findings in our report is not about attacker behavior. It's about internal respons...

The most important cybersecurity impact of artificial intelligence is not that attackers can write better phishing emails or automate parts of their workflow. It is that AI is changing the speed, scale, and decision-making dynamics of cyberattacks.  That creates a problem many organizations have not yet fully confronted: most cyber governance and incident response models were designed for human-speed attacks.  For years, security teams operated under a familiar sequence. Detect suspicious activity. Investigate. Validate the threat. Escalate to leadership. Decide on containment. Communicate with stakeholders. That model still has value, but it assumes defenders have enough time to build confidence before taking material action.  AI-enabled attacks challenge that assumption.  Adversaries can now use AI to accelerate reconnaissance, generate highly personalized social engineering, modify malware, test payloads, summarize stolen data, identify vulnerabilities, a...

For years, our industry has treated "blocking" as the gold standard. If the email didn't land, if the malware didn't execute, if the alert fired in the SIEM, we called it a win. That mindset made sense in a world where most attacks came through a handful of familiar doors. But AI has changed the game. We're not dealing with hobbyists sending out clumsy phishing attacks anymore. Modern adversaries are running multi‑channel, AI‑assisted businesses at machine speed. And if all you're doing is blocking at the edge, you're not really defending. You're just delaying. Generative AI has made it trivial to spin up highly personalized, multi‑step social engineering campaigns that operate simultaneously across email, collaboration apps, mobile, social media, and paid media. The result is a social engineering attack chain : a sequence of stages designed to manufacture trust, erode judgment, and bypass brittle controls. You don't beat that by tuning another filter. You have to disrupt the at...

Modern attacks are not primarily defeating infrastructure. They are inheriting trust. Identity Did Not Become Important. It Became Infrastructure. Security teams still talk about identity as though it is one security discipline among many, sitting beside endpoint protection, cloud security, network defense, and vulnerability management. That framing no longer reflects how modern enterprises actually operate. Modern business environments run on identity, delegated trust, cloud roles, automation pipelines, APIs, machine permissions, and continuously exchanged credentials. Users authenticate into SaaS platforms that the organization does not own. Workloads assume permissions that nobody provisions manually. Services trust other services built across years of acquisitions, migrations, technical debt, and operational compromise. The enterprise is no longer running on infrastructure alone. It is running on identity. Attackers recognized this shift before many defenders did. That i...

Launching a ransomware attack used to take real effort. Now, thanks to AI, almost anyone can launch a sophisticated attack, which changes the game for everyone responsible for protecting businesses. Reconnaissance that once took hours now takes minutes. Phishing emails that used to require careful crafting can now be generated at scale and sent to hundreds of targets simultaneously. IBM's 2025 Cost of a Data Breach Report found that AI reduced the time required to create phishing emails from 16 hours to just 5 minutes. For MSPs managing dozens or hundreds of clients, and for internal IT teams holding the line across an entire organization, understanding how AI is changing ransomware is key to staying ahead of the threat and minimizing disruption when attacks occur. The attack that starts in the inbox Before attackers can encrypt files or demand a ransom, they first need a way into the organization. One of the easiest ways to get that access is by tricking someone into cli...

The disclosure of CVE-2026-25177, a high-severity privilege escalation flaw in Microsoft Active Directory Domain Services, is a timely reminder that identity infrastructure remains one of the most consequential attack surfaces in the modern enterprise. Rated HIGH with a CVSS score of 8.8, this vulnerability allows an authenticated domain user to escalate privileges and move laterally across the network without elevated starting permissions or any user interaction. The mechanics are instructive. If a compromised account holds native Active Directory (AD) permission to modify Service Principal Names (SPNs), an attacker can create a duplicate SPN for a targeted service. When clients request Kerberos authentication, the domain controller may issue a ticket encrypted with the wrong key, causing a denial of service or forcing a fallback to the weaker NTLM protocol. No access to the targeted server is required beyond that initial SPN-write permission. In an environment where Active Directo...

The structural flaw in detection-only security postures runs deeper than tooling choices. Every hour a security team spends triaging runtime alerts is an hour not spent governing what entered the pipeline in the first place. And in modern CI/CD environments, that means the handful of alerts that represent genuine software supply chain compromise arrive only after the malicious dependency has already executed its payload, exfiltrated credentials, or established persistence inside the environment. The industry built an entire market category on that backwards logic, and enterprises are now paying for it in breach costs, developer burnout, and regulatory exposure that carries personal liability for the security leaders whose names appear on the program. The shift that actually reduces risk is not better monitoring at the end of the pipeline; it is governing the point of ingestion before code ever enters your lifecycle, which is a fundamentally different problem requiring a fundamental...

Microsoft Teams' cross-tenant collaboration feature, which allows external accounts to message employees directly, is enabled by default in most enterprise deployments . Most organizations have never audited or restricted it. That default setting has become one of the more reliable social engineering entry points security teams are managing today. The base attack is straightforward. An attacker creates an external Teams account, identifies a target through LinkedIn or a company directory, and sends a message posing as IT helpdesk staff. The message cites an urgent account issue (an MFA problem, a security alert, a failed login) and asks the employee to open Quick Assist, a built-in Microsoft remote assistance tool, and approve a session. What has changed recently is the layer added on top of that initial contact: an AI-generated voice that sounds like someone the target already knows. How the Base Attack Chain Unfolds Once Quick Assist access is established, the attack fol...

AI is changing the security landscape. More and more threat groups incorporate LLMs into their reconnaissance and exploitation workflows. The notion that some vulnerabilities are too complex to implement is now obsolete. Using LLMs, hackers can automatically find and exploit complex vulnerabilities. We have all heard of Claude Mythos and its ability to identify vulnerabilities in large codebases and exploit them automatically. But LLMs can do more than find vulnerabilities in code. ShinyHunters has scanned thousands of Salesforce Sites. They used a modified version of "AuraInspector". They possibly used an LLM to code their framework, mods, reconnaissance tools, and other aspects of their workflow. But the next step is to use AI to supercharge the attack process itself. We at Reco decided to explore what it would look like. Reco's security research team built an AI-powered agent capable of performing end-to-end security assessments of Salesforce Experience Cloud sit...

Phishing and insider threats continue to pose significant, often overlapping risks in modern threat landscapes. Compromised credentials obtained through phishing campaigns or social engineering attacks can grant adversaries legitimate access, effectively converting external threats into insider risks. This convergence complicates detection, as malicious activity may closely mimic authorized user behavior. To address this challenge, security teams require a platform that can correlate events from multiple sources, including endpoints, users, and network activity. Security teams, therefore, need a Security Information and Event Management (SIEM) platform that can collect and correlate activity across endpoints, users, and network sources. A SIEM platform enables the aggregation and correlation of data from multiple sources, helping analysts uncover suspicious patterns that would otherwise go unnoticed. Phishing attacks Phishing attacks remain among the most effective techniques us...

New Bitdefender research reveals that 97.7% of respondents now use endpoint detection and response (EDR). That number might seem high compared to commonly accepted market penetration estimates, particularly for mid-market organizations. But it is further confirmation that the vast majority of businesses have already upgraded their endpoint protection. This is hardly surprising. The conversation in enterprise security is no longer just about blocking malware or stopping known threats. It is about "proving" that an organization can detect, investigate, and respond to modern attacks before they escalate into operational disruption, financial loss, or reputational damage. This shift was driven by a new reality: endpoint protection alone is no longer enough. The laggards, typically mid-market organizations with lean IT and security teams, are now realizing this. Threat actors are AI-enabled, more evasive, and increasingly successful at bypassing traditional defenses. At the...

Most identity verification failures do not originate from flawless synthetic IDs or visually undetectable deepfakes. Instead, they stem from structural exposures and information loss between the point of data capture and the final automated decision. As remote identity systems evolve, trust becomes an architectural property. If a backend cannot verify the hardware provenance of an image or video, the rest of the security pipeline operates on degraded input. By the time a synthetic face reaches a visual liveness model, the most critical context may already be gone. This post examines why fragmented identity APIs drop vital signals, how identity supply chains dilute accountability, and why these gaps allow digital injection attacks to succeed. The Hidden Risk in Identity Supply Chains Modern identity verification often relies on a complex supply chain that distributes camera capture, document parsing, liveness checks, and risk scoring across multiple vendors. Rather than opera...