La analítica predictiva transformará el campo de la ciberseguridad

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CLAVES:

  • La disrupción en el campo de la ciberseguridad viene de la mano de la analítica predictiva.
  • Hasta hoy, las soluciones de seguridad informática se basaban en rastrear ataques que ya se habían producido.
  • La analítica predictiva permite anticiparse al enemigo y evitar el ataque antes de que efectivamente tenga lugar.
  • Sin embargo, no son pocos los obstáculos para esta tecnología. El volumen ingente de datos y la sofisticación de los cibercriminales no facilitan la tarea.

Los expertos en ciberseguridad y analistas varios están tratando constantemente de mantener el ritmo de la disrupción en el volátil y siempre cambiante sector de la seguridad informática. Pero, pese a la sofisticación de las herramientas utilizadas, los expertos son conscientes que se producen con frecuencia violaciones de datos privados, hasta el punto de que dedican más tiempo a investigar cuándo se producen esas brechas, que a esclarecer si efectivamente se producen. Y, aun así, son violaciones que pasan desapercibidas durante mucho tiempo.

Las soluciones basadas en machine-learning, es decir, en aprendizaje automático, están presentándose como posible solución, al reducir el tiempo que se tarda en detectar ataques. Pero hablamos, todavía, de identificar violaciones que ya se han producido. Así pues, ¿qué pasaría si pudiéramos adelantarnos al enemigo y predecir su siguiente ataque antes de que efectivamente tuviera lugar? Gracias a la potencia del análisis predictivo, esta idea podría acabar haciéndose realidad.

El análisis predictivo es la ciencia que más impulso está ganando últimamente en prácticamente todos los sectores, permitiendo a las empresas modernizar y reinventar su forma de hacer negocios. Esta tendencia al alza puede ser realmente útil en el campo de la ciberseguridad al ayudar a determinar la probabilidad de un ataque determinado sobre una empresa determinada, estableciendo así las defensas necesarias para evitarlo. Las empresas más punteras del sector están adoptando esta tecnología e incorporándola a su oferta de seguridad, y cada vez quedan más pocas dudas en torno al potencial disruptivo de la tendencia.

Más allá de las firmas

El enfoque tradicional de la lucha contra los ataques cibernéticos se basa, fundamentalmente, en reunir datos sobre el malware conocido y crear firmas digitales, que resultan comparadas con el tráfico en red, los archivos y los mensajes de correo electrónico con el fin de detectar posibles amenazas. Pero este sistema de firmas, siendo necesario, no es suficiente, especialmente para hacer frente a ciberdelincuentes cada vez más sofisticados.

En la última década el panorama de las amenazas contra la seguridad cibernética ha cambiado drásticamente“, reconoce Amir Orad, CEO de Sisense. “Los ciberdelincuentes han pasado de ser adolescentes aburridos a redes organizadas con todo tipo de infraestructuras tecnológicas, altamente sofisticadas, y con objetivos concretos“. De hecho, hasta tal punto son elaborados los ataques que, según un informe de Verizon, más del 50% de las violaciones de datos privados continúan sin descubrirse meses después.

No obstante, el problema va más allá. “Las soluciones de ciberseguridad actuales son del todo insuficientes“, dice Doug Clare, vicepresidente de FICO. “Es como tener una alarma antirrobo que no se activa hasta que el ladrón ha terminado su trabajo, se ha alejado del lugar y ha huido“.

Encontrar la aguja en el pajar

A pesar de ser una tendencia sin duda prometedora, el análisis predictivo es complejo y presenta requisitos realmente complicados de afrontar. Por un lado, el volumen de datos involucrados en la identificación y predicción de amenazas es ingente, por lo que se necesitan herramientas con una enorme capacidad de almacenamiento, memoria y computación. “Las empresas, a día de hoy, trabajan con gigantescas cantidades de datos procedentes de múltiples fuentes, lo que hace especialmente difícil rastrear la señal de un ciberataque, pues para hacerlo en tiempo real sería necesario analizar todos esos datos y realizar cálculos avanzados de forma instantánea“, explica Orad, de Sisense. “Son los desafíos clásicos de la analítica predictiva“, añade Lucas McLane, director de tecnología en SparkCognition. “Esto se debe a que el procesamiento analítico predictivo requiere muchos más recursos computacionales, especialmente cuando hay que operar con datos a gran escala“.

Para complicar todavía más la situación, Orad advierte que “el hecho de que la señal del ataque sea normalmente muy débil, y esté rodeada de una enorme cantidad de ruido generado por la propia empresa, provoca que el cambio en los patrones reconocibles sea mínimo. De hecho, de ajustar unos parámetros tan restrictivos, los falsos positivos serían más frecuentes que los positivos reales“.

Es por todo esto que las empresas de seguridad se están asociando con firmas de análisis de datos, con tal de crear herramientas de nueva generación que utilicen el Big data para identificar patrones sospechosos y, antes de que se haga efectivo el ataque, se tomen automáticamente las medidas de seguridad necesarias para prevenirlo.

Análisis predictivo y machine-learning

El análisis predictivo aplicado a la ciberseguridad proporciona previsión, pero no garantías“, señala McLane. Es por ello que, para este experto, este tipo de soluciones deben integrar tecnologías de machine-learning, con el fin de aprovechar todo su potencial. En este sentido, algunas compañías están creando sistemas de canalización cognitiva, que consiste en análisis predictivos basados en machine-learning, lo que permite identificar mucho más fácilmente una amenaza real a partir de valores atípicos.

Pero no todo el mundo cree que el análisis predictivo sea la solución definitiva. Arijit Sengupta, CEO de BeyondCore, sugiere que es necesario acercarse a la problemática desde una nueva perspectiva. Para él, los desafíos en materia de ciberseguridad se basan en dos factores: el volumen de datos en constante crecimiento, por una parte, y la sofisticación de los criminales, por otra. Si bien el análisis predictivo puede ayudar a solventar problemas a día de hoy incontestados, nos llevará también a un escenario con nuevas problemáticas. “Si el volumen de datos crece, y por tanto lo hacen los recursos accesibles para los atacantes, el análisis predictivo se muestra insuficiente“, opina Sengupta. Así, lo fundamental sería replantearse qué, cómo y dónde almacenamos los datos.

Otro factor que no puede olvidarse, y que se apresura a señalar Olivier Tavakoli, es que las herramientas de los cibercriminales avanzarán paralelamente a las soluciones de seguridad informática. De hecho, ya es posible pensar en formas de penetrar en un sistema a pesar de contar éste con herramientas de seguridad basadas en la analítica predictiva, como por ejemplo el ataque por patrón aleatorio.

El futuro del análisis predictivo

Sea como sea, tras analizar el papel que pueden jugar la analítica predictiva y el machine-learning en el sector de la ciberseguridad, y ver que estas tecnologías están transformando múltiples industrias a nivel global, es justo estimar que tendrán un papel fundamental en el futuro de la seguridad informática. “Nos hallamos en posición de afirmar que en un futuro próximo no habrá seguridad sin análisis predictivo“, añadía Orad. “Las amenazas son tan sofisticadas y cambian tan rápidamente que la única forma de identificarlas a tiempo es a través del Big data en tiempo real“.

TECH CRUNCH. How predictive analytics discovers a data breach before it happens. 26 de julio de 2016 [Consultado 27-07-2016]. Disponible en: https://techcrunch.com/2016/07/25/how-predictive-analytics-discovers-a-data-breach-before-it-happens

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