  Intelligent reflection surface (IRS) is emerging as a promising technique for
future wireless communications. Considering its excellent capability in
customizing the channel conditions via energy-focusing and energy-nulling, it
is an ideal technique for enhancing wireless communication security and
privacy, through the theories of physical layer security and covert
communications, respectively. In this article, we first present some results on
applying IRS to improve the average secrecy rate in wiretap channels, to enable
perfect communication covertness, and to deliberately create extra randomness
in wireless propagations for hiding active wireless transmissions. Then, we
identify multiple challenges for future research to fully unlock the benefits
offered by IRS in the context of physical layer security and covert
communications. With the aid of extensive numerical studies, we demonstrate the
necessity of designing the amplitudes of the IRS elements in wireless
communications with the consideration of security and privacy, where the
optimal values are not always $1$ as commonly adopted in the literature.
Furthermore, we reveal the tradeoff between the achievable secrecy performance
and the estimation accuracy of the IRS's channel state information (CSI) at
both the legitimate and malicious users, which presents the fundamental
resource allocation challenge in the context of IRS-aided physical layer
security. Finally, a passive channel estimation methodology exploiting deep
neural networks and scene images is discussed as a potential solution to
enabling CSI availability without utilizing resource-hungry pilots. This
methodology serves as a visible pathway to significantly improving the covert
communication rate in IRS-aided wireless networks.
