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AI agents have become incredibly useful across many industries by their ability to reach inboxes, documents, and financial information, then search, summarize, or act on what they find. But that access creates a data-in-use problem: conventional encryption protects information in storage and transit, but an agent generally needs it decrypted in memory while processing it, where sensitive material can be exposed to application code, logs and debugging systems, infrastructure operators, or a compromised host. Conan Yu's work offers one practical response to that problem. He is co-founder of Rena Labs , which develops infrastructure for confidential AI training and inference using trusted execution environments (TEEs), hardware-isolated environments designed to limit the surrounding host's access while code and data are processed. Over roughly two years, Yu has worked across hardware and cloud TEE deployments, privacy-preserving financial-data analysis, and private AI inference. He a...

As organizations deploy AI coding agents across large monorepos and microservices environments, a fundamental problem emerges: the model may be capable of making the change, yet still lack the organizational context required to make the right change safely. A developer can ask an AI coding agent to deprecate an API field, update an authentication flow, or modify a service interface. The agent can inspect the code available on the developer's machine and search for references. What it may not know is that the field is consumed by four other services across separate repositories, that one of those services belongs to another team, or that the same field eventually carries sensitive data into a third party integration. This is not simply a context window problem. It is a code context problem: providing AI agents with accurate, current, organization wide evidence about how software actually behaves. One emerging approach is to generate that evidence directly from source code us...

Many AI systems depend on input signals that teams cannot fully inspect or explain. These opaque sources reduce visibility into the data paths that influence model behavior. Engineers lose provenance records, limiting the diagnosis of abnormal outputs. This complicates the work of security teams that need clear records of what influenced a model at any point in time. Verifiable search data offers a stable alternative. It gives teams an input they can examine, store, and reproduce in controlled conditions. Engineers can compare model behavior against information that was publicly accessible at the time a result was produced, rather than depend on hidden internal signals. This article outlines why verifiable search data gives AI and security teams the clarity required to maintain operational control. Why Traceability Matters in AI Systems Traceability lets teams follow an input from its origin through each processing step. When every stage can be inspected, engineers can review...

Artificial intelligence is everywhere now. From automated code completion to autonomous infrastructure management, AI tools and AI agents help DevOps speed up deployment cycles and change how development teams operate in general. At the same time, this rapid adoption of AI has created a reality that is hard for security teams to ignore: as with the growth of AI capability within the software development life cycle, the attack surface also grows. In 2025, there were 68 AI-related incidents recorded across major DevOps platforms according to the 2026 DevOps Threats Unwrapped Report . In the first half of 2026, the number of AI-related incidents visibly grew — research from GitProtect Lab tracked 84 AI-related incidents in six months alone. Thus, comparing the first half of 2026 to the same period in 2025 shows that AI-related incidents in development environments have nearly tripled. What do DevOps and DevSecOps say about AI incidents in general? According to GitProtect Lab 's surve...

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.

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...

I have been a Star Wars fan since the moment I took my seat in the theatre and saw Princess Leia's rebel ship trying to outrun an Imperial Star Destroyer. It's impossible to see that movie (or its greatest successor, Andor ) and not take the side of the underdog rebels, who are determined to escape the iron fist of imperial control. Of course, in my work as a security professional, "control" is the name of the game. I've spent as much of my career trying to stop my own end-users from going outside the lines as I have trying to guard against malicious outsiders. I personally still think I'm the good guy, since my ultimate goal is to protect sensitive data, but I understand why IT and security teams are often seen as the bad guys. After all, we do operate according to something called the "rule of no." It's not great branding, and increasingly, it just isn't working. Here's the situation in 2025: we have a galaxy's worth of diverse applications, devices, and user identities accessing...

Nowadays, managing who has privileged access to your most critical data and systems is more important than ever. Privileged access serves as the key to your organization's most sensitive assets, making it a high-value target for malicious actors. Any misstep in securing this access might lead to privilege abuse and serious data breaches. But it's not just about defending against cybercriminals; poor management of privileged access can also result in operational disruptions, costly downtime, and non-compliance with industry regulations. To truly master Privileged Access Management (PAM) , you need more than just basic controls in place. You need a proactive, multi-layered approach that reduces risks without slowing down your operations. Let's discuss what best practices you can use to secure your privileged accounts and stay one step ahead of potential threats. Why is privileged access management so important? Privileged access management is an essential pillar of a robust c...

Compliance professionals today are dealing with numerous challenges. At the same time, their companies face increased scrutiny and cyberthreats, and compliance teams have fewer resources and reduced headcount. It's a lot for even the most sophisticated and experienced teams to manage. As a result, compliance professionals are seeking out ways to do more with less. Sometimes the solution is utilizing technology, such as automated software tools that streamline processes or leveraging AI for greater efficiency. In other circumstances, individuals responsible for compliance are choosing an easy path to simply check the box on compliance with a flimsy, budget audit. This may be enough to get the C-suite off their back, but it leaves the company open to significant risk. Each year, A-LIGN surveys hundreds of compliance leaders to learn more about the current state of compliance and better understand the factors that impact their decisions. What are the driving forces behind their complia...