2. Outline
• SIEM infrastructure.
• Brain’s Memory.
• Goal oriented.
• Machines and Data.
• Directed Graphs.
• The V’s of Big Data.
• Data gathering.
• Building a timeline.
5. Outline
• Adaptive. This would allow the machine to learn from changes as new goals and
requirements evolve. The engine, too, could also cope with unpredictability and
ambiguity, and make reasoned decisions. This adaptability would allow pre-defined
rules to be changed and migrated over time, and would also support the
strengthening of security when there is a perceived attack, and to reduce it when
not under attack.
• Interactive. This would support the interaction of the cognitive engine with a
whole range of services, people, systems, and so on. A core part of a cognitive
engine - as we see in the brain - is the ability for it to take inputs from a range of
sources, and then provide outputs in the required way.
• Iterative and stateful. This involves understanding previous interactions and be
able to sustain future ones, along with plotting the best course and to learn new
routes. As humans, our interactions with others are often stateful, and where we
remember where we have left things with different people. Within a cognitive
engine, we would thus define our interactions as well-defined states, of which we
move into and out of.
• Contextual. This allows the identification of key contextual elements within the
data, including locations, names, dates, and so on. The original data may be in
many different formats and could be structured, semi-structured or unstructured..