
Download Our Report on Account Takeover
Because of widespread password reuse, Account Takeover (ATO) attacks have become an extremely lucrative business for cybercriminals.
WHITEPAPER
Over the years, security professionals have learned surprising lessons about how password policies affect user behavior. Faced with complicated password requirements and hundreds of online accounts to keep track of, people often take dangerous shortcuts—and criminals benefit.
To help organizations mitigate the risk posed by users’ bad password habits, the National Institute of Standards and Technology (NIST) designed a set of password guidelines that balance security and usability. The updated guidance abandons the long-held philosophy that passwords must be long and complex. In contrast, the new guidelines recommend that passwords should be “easy to remember” but “hard to guess.” According to NIST, usability and security go hand-in-hand.
Read this white paper to understand what NIST’s guidance means for your organization, including:
Align with the latest password security guidelines from the National Institute of Standards and Technology (NIST).
Understanding the Latest NIST Password Guidelines
A few of our happy customers:
Because of widespread password reuse, Account Takeover (ATO) attacks have become an extremely lucrative business for cybercriminals.
Enterprises are trying everything they can to prevent cyber criminals from taking over employee and customer accounts to gain access to data and systems.
Download the ebook, 6 Myths about ATO Prevention Strategies to learn which of the most common techniques and technologies help and which provide false hope.
Current, Relevant, Truly Actionable Data
SpyCloud’s account takeover prevention and fraud investigation solutions are backed by the world’s most current and comprehensive repository of recovered stolen credentials and PII. More data, particularly plaintext passwords, means more matches and stronger account protection.
See your real-time breach exposure details powered by SpyCloud data.
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