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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....

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 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...

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...

AI-driven vulnerability discovery is no longer a research project. Claude Mythos proved that. In a single sweep, it uncovered thousands of vulnerabilities in software we use every day, generated working exploits, and exposed bugs that had survived decades of human review. Other AI models are rapidly catching up, and we've entered into an entirely new operating environment for cybersecurity. The industry is treating this as a turning point, and it is. But not for the reason most people might think. The Real Problem Was Never Finding Vulnerabilities Most of the conversation around AI security focuses on discovery: AI can now identify vulnerabilities faster than human teams ever could. That is certainly true, but it also misses the larger operational reality organizations have been struggling with for years. Security teams were already overwhelmed long before AI entered the picture. Vulnerability scanners, fuzzers, and static analysis tools have consistently generated more...

For most managed service providers (MSPs), ransomware recovery is not a problem that affects one client at a time. It is a multitenant, high-pressure scenario where recovery failures impact multiple clients at once. Testing ransomware recovery is not just a technical exercise but a business-critical requirement. The green check of a successful backup job does not guarantee successful ransomware recovery. Attackers today do more than encrypt files. They compromise identity systems, alter configurations, and create persistence mechanisms that survive system restoration. So, a "clean" backup can still reintroduce dormant malware or broken dependencies into your environment. Recovery success depends on whether systems are usable, trusted and operational after restore, not whether data simply exists. Modern ransomware protection and recovery strategies require correlation between security events and backup data. Without that, MSPs are forced into guesswork across multiple cl...