Bounds on the Chi-Square Distribution Tail, need upper bound for probability

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I have the following probability bound I want to prove but don't know what bound the author is using.

say $N_k \sim \mathcal{N}(0,\sigma^2)$

Then, we are interested in upper bounding the probability:

$\mathbb{P}\left( \sum_{k=1}^n N_k^2 \geq n^3 \right)$.

The resulting upper bound in the paper is :

$\mathbb{P}\left( \sum_{k=1}^n N_k^2 \geq n^3 \right) \leq \frac{\sigma^2}{n^2}$

Any clue or material you guys may refer me to?

What I tried, no too fancy, just dividing both sides by $\sigma^2$, then we obtain a sum of squared normal Gaussian variables, and thus, a chi-squared. Then:

$\mathbb{P}\left( \sum_{k=1}^n N_k^2 \geq n^3 \right) = \mathbb{P}\left( {\chi}^2_n \geq \frac{n^3}{\sigma^2} \right)$.

Then, don't know how to proceed ??

Thanks