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This chapter presented numerous examples of how to configure the core DBV components to protect an example object-owner account These examples illustrate the effectiveness of the new DBV security features Product features such as DBV realms, DBV command rules, and DBV SARs are critical for addressing many of the issues we face today around consolidation, compliance, and separation of duties These features serve as effective defensive measure for cyber security by limiting the extent to which statements and privileges can be exploited by an attacker We demonstrated how DBV realms filled in a gap in the ability to place boundaries around the use of system ANY privileges and how simply defining this realm boundary offers immediate protection against inadvertent access to the objects being protected by the realm We examined how command rules allow us to implement security policies based on the real business rules we may have defined in our organization We also demonstrated how DBV SARs can restrict an administrator s or an application s ability to enable sensitive privilege sets This feature reduces the threat of a privilege set being used outside of the context in which the usage was intended Database administrators should consider updating their production databases to DBV to meet current and future security threats and challenges The declarative policy configuration of the product s security rules, based on multiple factors, helps ensure the appropriateness of an organization s defense against these threats and challenges as they change over time Once DBV policy is deployed, database security administrators should consider the serious nature of what a violation could mean and question the legitimacy of it We examined the ability to control policy evaluation semantics, auditing configuration, and custom event handling that allow these administrators to refine policies or respond to these violations when they occur.

Now suppose the two of them play this game repeatedly If Row always makes the same move, Column will quickly catch on and will always play the countermove, winning every time Therefore Row should mix things up: we can model this by allowing Row to have a mixed strategy, in which on each turn she plays r with probability x 1 , p with probability x2 , and s with probability x3 This strategy is speci ed by the vector x = (x 1 , x2 , x3 ), positive numbers that add up to 1 Similarly, Column s mixed strategy is some y = (y 1 , y2 , y3 )2 On any given round of the game, there is an x i yj chance that Row and Column will play the ith and jth moves, respectively Therefore the expected (average) payoff is Gij Prob[Row plays i, Column plays j] = Gij xi yj

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The Oracle Data Guard logical standby database is a logical replica of the primary database, which is kept in sync using the Data Guard SQL Apply process The SQL Apply process converts the transactions from the primary database redo logs to SQL statements, and the SQL is then applied on the logical standby database to replicate the transactions The logical standby database can have different physical characteristics than the primary database, and can even be on different versions Using this capability, the source system can be migrated by creating a logical standby of the source on the Database Machine and then performing a Data Guard switchover to the logical standby database

Part II:

Row wants to maximize this, while Column wants to minimize it What payoffs can they hope to achieve in rock-paper-scissors Well, suppose for instance that Row plays the completely random strategy x = (1/3, 1/3, 1/3) If Column plays r, then the average payoff (reading the rst column of the game matrix) will be 1 1 1 0 + 1 + 1 = 0 3 3 3 This is also true if Column plays p, or s And since the payoff of any mixed strategy (y 1 , y2 , y3 ) is just a weighted average of the individual payoffs for playing r, p, and s, it must also be zero This can be seen directly from the preceding formula, Gij xi yj =

The source database can be 10103 or later on a Linux x86-64 platform The outage time on the source database is the time it takes to perform a Data Guard switchover to the standby database This method allows you to perform an upgrade of the database during the migration process You can also use this method to upgrade from HP Oracle Database Machine to the Oracle Exadata Database Machine with minimal downtime The SQL Apply process does not support all data types and objects If you have unsupported objects and data types in your source database, you should consider using methods like Data Pump for migration of those objects For a complete list of unsupported data types and objects, refer to the Oracle Data Guard documentation Since the SQL Apply process requires more work on the target system than the Redo Apply used for physical standbys, you must make sure that the target machine can keep up with the changes sent to it, although with an Exadata Database Machine as the target, the chances of a failure in this area are slight

his chapter introduces design techniques and patterns for DBV security controls that will be incorporated into the process of creating a new application We first introduce a notional database application environment that is used to describe design techniques for analyzing the applications in this environment The analysis will uncover opportunities for you to apply DBV security controls The design techniques include documentation components, rules, and patterns that will be applied during the design phase of new database application

yj 0 = 0,

Oracle GoldenGate can replicate data between two Oracle Databases running on different versions and platforms This feature allows you to migrate and upgrade a prior version of Oracle Database on any platform to the Database Machine and keep the databases in sync for a time, and then perform a switchover once the destination is ready to go live The benefits of using Oracle GoldenGate for migration are

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