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A security leader at a global finance company told us recently that his team discovered three times more AI tools running in their environment than IT had approved. Nobody had smuggled them in. Employees had simply pointed agents at their work, and the agents brought their own tools with them. That conversation is not unusual. It is the conversation. Over the past year, in customer discussions across finance, healthcare, manufacturing, and government, the same four struggles come up so consistently that we have started treating them as the shape of the problem itself. Every company effectively hired a second workforce this year, human workers and agentic workers side by side, and the agentic workers never went through onboarding. No handbook, no scoped credentials, no acceptable-use policy they can actually read. Here is what teams are struggling with, what our research says about why, and what closing each gap actually requires. Struggle one: "I can't tell you what age...

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...

How session cookie theft bypasses MFA — and what you can do about it When you check into a hotel, you show your ID at the front desk. The clerk verifies who you are, maybe checks a secondary piece of information, and hands you a key card. From that point on, that key card is what gets you into your room. It doesn't matter that you proved your identity at check-in. What matters is who has the key. Your applications work the same way. When a user logs into a web application — entering their password, completing an MFA challenge — the application issues them a session token, typically stored as a cookie in their browser. That token is their key card. Every subsequent request the user makes, the application checks for the token, not the credentials. If the token is valid, access is granted. And if someone steals that token? They get in, too. No username required. No password required. No MFA prompt. They simply ...

Identity platforms are an organization's backbone of secure access. But what happens when you need to migrate from one identity provider to another? The stakes are high: downtime, broken integrations, compliance gaps, and user frustration. Yet, with the right strategy and preparation, these fears can be addressed head-on. Identity migration is intimidating If a migration is poorly planned and executed, it can mean lost productivity, frustrated customers, and a hit to your business' bottom line. Identity touches every corner of an organization, from HR systems to customer portals. Beyond downtime, migrations often expose brittle integrations, undocumented dependencies, and legacy technologies that no one wants to touch. Add compliance requirements (SOC2, HIPAA, DORA) and the pressure mounts. Even well-planned transitions can surface unexpected issues, edge cases, and broken processes. Why consider identity migration at all? Why migrate at all? Often, it's about cost savings, strea...

The proliferation of AI agents in the enterprise has moved from theoretical to practical at a remarkable pace. These agents, whether developed internally or licensed, are increasingly integrated into core business workflows. While they promise substantial gains in automation and productivity, they also introduce a new and complex class of security risks that demand immediate attention.  The core challenge is not whether to adopt AI agents, but how to govern them effectively. A disciplined approach to balancing innovation with security is essential for any organization looking to leverage AI without exposing itself to unacceptable risk.  Recent research highlights the urgency of this issue. A comprehensive study found that 82% of companies are already using AI agents, with 53% acknowledging they access sensitive information daily . This rapid adoption, often occurring without adequate oversight, creates significant vulnerabilities. The imperative is clear: organizations must...

In a matter of months, AI became a tool relied on for daily critical tasks. Now, we are seeing it used just as easily to attack systems, deceive users, or even manipulate data. While full capabilities are still being explored, the most significant threats posed by AI are yet unknown. Even without knowing exactly what's coming, organizations can take meaningful steps now and develop identity security strategies to defend against AI-driven threats to avoid being an easy target. Suggestion 1: Start with the basics In the face of unknown AI-driven threats, one of the best places to start is with an identity security strategy that addresses the fundamentals.  When it comes to identity and access management, solutions that offer 'Preemptive Defense' (a term coined by Gartner) allow detection and protection before a user even authenticates to your systems. Think IP reputation checks, web application firewalls, machine learning-based risk scoring, user policies and app policies. Risky...