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Netwrix 2026 Data and Identity Security Report cover, a geometric mosaic in dark navy and teal with the report title and subtitle.

Netwrix: Companies Deploying AI Breach At Four Times The Rate Of Peers

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Fast AI adopters breach at 43%, four times the 11% rate of slower peers, despite outperforming them on every security fundamental Netwrix measured.

Key Takeaways

  • Companies that have broadly deployed AI breach at 43%, four times the 11% rate for companies that have not
  • Fast AI adopters are ahead on security fundamentals, yet they breach more; AI deployments are creating new automated accounts and AI agents faster than security teams can track and control them
  • 76% of organizations do not fully track or control the AI agents and automated systems running inside their networks
  • 41% already have AI agents running in production, with those agents accessing company data on behalf of employees
  • 74% have no clear picture of which AI agents and accounts can access their sensitive data
  • Only 11% have AI governance that is fully enforced with consistent controls over who and what can access company data, applied in real time
  • Mid-size companies (500-999 employees) report the highest breach rate of any company size at 40%

Netwrix, a data security company, released its 2026 Data & Identity Security Report, finding that organizations in which AI has significantly increased the number of digital identities in their environments reported a 43% breach rate over the past 12 months. In security terms, an identity is any account with access to company systems: a human employee, an AI agent, a bot, or an automated service. Organizations where AI has not meaningfully expanded that number reported 11%. Netwrix surveyed 2,317 security and IT professionals across more than 60 industries worldwide.

 

Fast AI adopters are ahead on security fundamentals, yet they breach more

The gap is not explained by weaker security practices among faster AI adopters. Organizations leaning hardest into AI are measurably ahead on identity security fundamentals:

  • 46% lack a current picture of what data they hold and where it is stored, versus 60% among slower peers
  • 75% have strong controls over AI agents and automated systems with access to company data, versus 56% among slower peers
  • Only 13% cannot see which AI tools employees are using without company approval, versus 25% among slower peers

“They invested in the playbook. They got breached anyway,” wrote Grady Summers, Netwrix chief executive officer and a former Fortune 500 chief information security officer (CISO), in the report’s executive summary.

 

AI creates identities faster than governance tools can keep up

Summers says the problem is structural. Identity governance tools were built for environments where identities grew at the pace of hiring. AI agents, copilots, and automated services add new identities at the pace of software deployment. Every new agent is a new identity requesting access to sensitive data. Static or periodically reviewed permission models were not designed for that cadence.

The supporting data reflects that gap. 76% of respondents say they do not fully govern or monitor non-human identities in their environments. 41% say they already run AI agents in production, with those agents accessing company data on behalf of employees. 74% lack a single, unified view of sensitive data and which identities can reach it.

 

Mid-market breach risk is highest; only 11% have reached full governance maturity

The breach risk is highest in the mid-market. Organizations with 500 to 999 employees reported a 40% breach rate, the highest among company size ranges in the survey. Large enterprises with 10,000 or more employees reported 27.3%. Organizations with fewer than 500 employees reported 17.8%.

The report maps organizations across five AI governance maturity tiers. Just 11% have reached the level the report calls fully operational, meaning enforced, continuous, and proactive governance over every identity and the data it can reach.

 

With 41% of organizations already running AI agents in production and just 11% at full governance maturity, most are still building the controls that AI is already outpacing.

Black-and-white portrait of Pi co-founders Guy Arazi and Yoni Ramon, whose AI security startup raised $35 million

Pi Raises $35 Million To Automate Software Security Fixes

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The company says its platform repairs security flaws the way an engineer would, and customers report cutting triage time by up to 80%.

Pi, an AI security startup, announced it raised $35 million to automate the repair of software security flaws. Calcalist reports the total includes a $25 million Series A led by Third Point Ventures and a $10 million seed round closed in early 2025, led by Brightmind Partners. Investors also include CrowdStrike CEO George Kurtz and Armis founders Yevgeny Dibrov and Nadir Izrael.

Pi’s platform builds what the company calls a “security brain”: a memory of an organization’s source code, cloud infrastructure, design documents, Slack and Microsoft Teams discussions, and past security incidents, including how each one was fixed. The company says that context lets the platform separate flaws that pose real risk from false alarms, then write and apply fixes the way an engineer would. Pi says customers cut the time of sorting real warnings from false alarms by up to 80%.

The company says its users include one of the world’s leading AI labs, cybersecurity companies, a major social network, and companies in the travel and insurance sectors. It did not name them.

Pi was founded in 2025 by CEO Guy Arazi and Chief Product Officer Yoni Ramon. Arazi was a security researcher at Microsoft, where he built defenses and vulnerability detection for Microsoft Defender, Azure, and the Microsoft Security Response Center. Ramon led offensive security at Tesla for more than a decade, running security projects for the company’s vehicle and robotics programs in the US, Europe, and China. He also worked in systems engineering at SpaceX and on projects at other companies led by Elon Musk, including the acquisition of Twitter (now X).

“Models like Anthropic’s Claude-Mythos can now uncover security flaws at a scale no human team can match, but that’s only half the equation for defenders,” said Arazi. “Pi enables organizations to remediate vulnerabilities as quickly as they can be identified.”

Pi has offices in San Francisco and Tel Aviv.

Hand-drawn illustration of a laptop with a keyhole on screen, representing AI-enabled cyber intrusion and network access risks, 2026.

Anthropic: AI-Enabled Attackers Are Going Deeper, and Harder to Detect

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AI is now running deeper inside attacks with less human direction. The old tools for spotting the most dangerous actors can’t keep up.

 

Hackers are using AI to penetrate compromised systems more deeply, and chain together attack stages with minimal human input. That combination of deeper access and more autonomy is making the standard signals security teams rely on to identify dangerous actors unreliable. Anthropic published those conclusions in a new report analyzing 832 accounts it banned for malicious cyber activity between March 2025 and March 2026.

 

AI is lowering the bar for getting in

Until recently, writing malware required real programming skill. AI has changed that. In Anthropic’s dataset, 67% of the 832 banned actors used AI to write malware. That matters because attackers who previously lacked the technical ability to build intrusion tools can now get them from a chatbot.

Once inside, AI is taking them further. AI use for account discovery rose 8.9% over the study period. Account discovery is how hackers map out which accounts exist inside a compromised network to find the credentials and access points they need to move deeper. At the same time, AI-assisted phishing, used to gain initial access, fell 8.6%. Attackers are spending less AI effort getting in and more AI effort going deeper once they’re there.

The result: the share of attackers classified as medium risk or higher jumped from 33% in the first six months of the study to 56% in the second.

 

Why it’s now harder to tell the dangerous ones from the rest

Security teams traditionally assess risk by counting how many distinct attack techniques an actor uses and what tools they access. Both signals have broken down.

In the dataset, the least-skilled actors averaged about 16 distinct techniques. The most skilled averaged about 20. Four techniques separate the bottom from the top. The platform used (Claude Code, API, or standard chat) also showed no correlation with an actor’s perceived danger.

What separates the highest-risk actors is whether they have built systems that let AI run the attack automatically, chaining together stages such as account discovery, lateral movement, and privilege escalation with minimal human direction at each step. Less-skilled actors are increasingly doing this too, which explains why the proportion of medium-risk-or-higher attackers nearly doubled in a single year.

That type of automated, multi-stage attack has no classification in the MITRE ATT&CK framework, the standard reference security teams use to track how attackers operate. Anthropic says it is in discussions with MITRE about updating the framework and has added safeguards to its most capable models to detect and block activities such as malware development and mass data exfiltration.