StoredIQ's ICM 5000 appliance takes it a big step further by recognizing text strings in unstructured data as meaningful object types, like names, places, dates and Social Security numbers, enabling policies like, "All files with Social Security numbers should be stored in the encrypted data store."
Eventually, we'll see a classification engine smart enough to recognize that a word-processing document with a date at the top, followed by a name, followed within six lines by another name, and the name at the bottom matching the name at the top, is a business letter. It could then do a database search to see if the intended recipient is a customer; if so, the engine would know this is a business letter subject to SEC Rule 17a-4 or other regulatory retention requirements. StoredIQ comes closest now, but no one has the LDAP linkup piece.
Ideally, your ILM classification engine would also know how frequently a file is accessed. There's a big difference between knowing that a file was last accessed on Friday and knowing it was accessed 104 times over the past 30 days, the last time on Friday.
Unfortunately, again, we're not there yet on common NAS and file server systems. A classification vendor could create a file system filter or similar agent that used a NetApp filer or EMC Celerra's antivirus scanning API to keep track of file-access frequencies, but this would require an agent be installed on every managed server.
As a workaround, this data also could be collected from an in-band NAS virtualization appliance like those from NeoPath Networks or Attune Systems. Because they see all file access requests, they can track access frequency and use that data as a trigger to migrate data to another location. Njini's IAM sits in-band looking at the CIFS data stream to collect stats, including who the file creator is, to classify files and place them in appropriate locations.
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