Privileged Access Management (PAM) and Data Analytics for Security
Cyber and network security are becoming a huge cause of concern for enterprises across a wide range of sectors. However, most of the attention of cyber security analysts remains focused on just user accounts, which is not the right approach in these conditions.
A generally ignored area in the domain of network and data security is Privileged Access Management (PAM) controls. These controls need to be put in place specifically for user accounts with privileged or high level network access.
The strongest reason for suggesting this approach is that accounts with privileged access cause way more damage in the event of a security breach. Secondly, as this is a generally neglected area, cyber miscreants tend to exploit it rather more.
A very helpful approach in this regard is implementing Least Privileged Access controls, even for the accounts with high level access. This approach emphasizes that access to critical data, controls management or other sensitive areas must be role specific.
In addition to the specific role of an employee, even in the case of privileged access rights, no further access should be granted to enterprise resources. This is otherwise a great approach and keeps the damaged well contained in the event of a security breach.
Another very important step is to leverage Machine Learning (ML) and Artificial Intelligence (AI) for accounts with privileged access. ML, when paired with user related data analytics, will reveal certain benchmarks or normal usage patterns.
Once these benchmarks or the bottom line for normal usage patterns have been achieved, AI can then be used to measure actual activity over the network and raise any red flags or abnormal usage patterns.
Lastly but most importantly, threat response to any potential misuse of an account with high level access needs to be automated. Any such controls that are in place need to be periodically reviewed and tweaked in the wake of emerging cyber threats.
