In this case, we can give an easy estimate on the tail probability of $T_1$. Notice that
$$ \{T_1 = 2n+1\} = \{S_1 \leq 0, \cdots, S_{2n-1} \leq 0, S_{2n} = 0, S_{2n+1} = 1\}. $$
Using one of the equivalent characterization of Catalan number, we can explicitly compute the probability of this event as
$$ \Bbb{P}(T_1 = 2n+1) = \frac{1}{2^{2n+1}}C_n = \frac{1}{2^{2n+1}(n+1)} \binom{2n}{n}. $$
From this, we explicitly compute the probability generating function of $T_1$ by
$$ |z| < 1 \quad \Rightarrow \quad \mathbb{E}[z^{T_1}] = \sum_{n=0}^{\infty} z^n \mathbb{P}(T_1 = n) = \sum_{n=0}^{\infty} \frac{C_n}{2^{2n+1}} z^{2n+1} = \frac{1-\sqrt{1-z^2}}{z}. $$
Letting $z \to 1^-$ shows that, by the monotone convergence theorem,
$$ \mathbb{P}(T_1 < \infty) = \lim_{z \to 1^-} \mathbb{E}[z^{T_1}] = \lim_{z \to 1^-} \frac{1-\sqrt{1-z^2}}{z} = 1. $$
Therefore $\Bbb{P}(T_1 = \infty) = 0$.
Addendum. Using this, we can also show that
$$ \mathbb{E}[T_1 z^{T_1}] = \frac{1-\sqrt{1-z^2}}{z\sqrt{1-z^2}}, $$
and so, $T_1$ infinite expectation $\Bbb{E}[T_1] = \infty$.
Unfortunately, I'm not aware of a nice (simple) bound to estimate the probabilities. (Note that the Borel-Cantelli lemma gives only a sufficient condition for $\mathbb{P}(\limsup_{n \to \infty} E_n) = 0$; so, in general, we cannot expect that the series converges.)
However, there is a simple way out: First consider the stopping time $\tau_k := \min\{\tau,k\}$ for fixed $k \in \mathbb{N}$. Obviously, $\tau_k$ is a bounded stopping time (so we have in particular $\tau_k<\infty$), and therefore it follows from the optional stopping theorem that
$$\mathbb{E}(S_{\tau_k}^2) = \mathbb{E}(\tau_k). \tag{1}$$
Since $\tau_k \uparrow \tau$ as $k \to \infty$, it follows from the monotone convergence theorem (MCT) that
$$\begin{align*} \mathbb{E}(\tau) = \mathbb{E}\left( \sup_{k \in \mathbb{N}} \tau_k \right) \stackrel{\text{MCT}}{=} \sup_{k \in \mathbb{N}} \mathbb{E}(\tau_k) &= \lim_{k \to \infty} \mathbb{E}(\tau_k) \\ &\stackrel{(1)}{=} \lim_{k \to \infty} \mathbb{E}(|S_{\tau_k}|^2). \tag{2} \end{align*}$$
As $|S_{\tau_k}| \leq N$, this shows in particular that $\mathbb{E}(\tau) \leq N^2$. This, in turn, implies that $S_{\tau}$ is almost surely well-defined. Applying the dominated convergence theorem in $(2)$ we conclude
$$\mathbb{E}(\tau)= \mathbb{E}(S_{\tau}^2).$$
Best Answer
You have not told us enough to reach your conclusions.
If you are saying each independent step is $+1$ with probability $p$ and $-1$ with probability $1-p$, with $p \lt \frac12$, then $S_n \not = -\infty$ for finite $n$, since $-n \le S_n \le n$. So $\Pr(T \lt \infty)=0$.
Since $p \lt \frac12$ you could conclude that $$\lim_{n \to +\infty} S_n = -\infty$$ with probability $1$ but that is not the same thing at all.