Parliament repeals .info filter

By
Follow google news

Internal concerns aired.

The Department of Parliamentary Services has re-instated internal access to 5.2 million websites with the .info top-level domain after concerns over the internal filter were made public.

Parliament repeals .info filter
Parliament House Canberra

The block, imposed on October 22 last year on advice from the Defence Signals Directorate, affected all officers, ministers and senators in Parliament House.

Though a blanket ban on all .info sites, the department had granted requests to access some 68 websites on the domain since the filter was put in place.

The office of department acting secretary, David Kenny, told ITnews that “the block has indeed been lifted”.

Timing and reasons for unblocking the websites were not given at time of writing.

However, staff at the office of Greens senator Scott Ludlam reported the parliamentary block was lifted a fortnight ago.

They said Senator Ludlam's office had been made aware of the block being lifted sometime after the senator raised concerns in Estimates earlier this month.

"It was private correspondence stating that the [department] had looked at Senator Ludlam’s concerns raised in estimates and that the block had been lifted," one staff member told iTnews.

Kenny had told a Senate committee in October that the block was put in place as the "domain is generally considered to be a source of more than its fair share of attacks and malicious software".

Add iTnews as your trusted source

Got a news tip for our journalists? Share it with us anonymously here.
Copyright © iTnews.com.au . All rights reserved.
Tags:

Most Read Articles

Poor WA gov M365 security led to $71k theft and children's data breached

Poor WA gov M365 security led to $71k theft and children's data breached

Health and Aged Care CISO retires

Health and Aged Care CISO retires

US medical device maker Stryker's Microsoft environment attacked

US medical device maker Stryker's Microsoft environment attacked

Services Australia describes fraud, debt-related machine learning use cases

Services Australia describes fraud, debt-related machine learning use cases

Log In

  |  Forgot your password?